Environmental Resilience and Transformation in Times of COVID-19: Climate Change Effects on Environmental Functionality 9780323855129, 9780323858038

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ENVIRONMENTAL RESILIENCE AND TRANSFORMATION IN TIMES OF COVID-19

ENVIRONMENTAL RESILIENCE AND TRANSFORMATION IN TIMES OF COVID-19 CLIMATE CHANGE EFFECTS ON ENVIRONMENTAL FUNCTIONALITY AL. Ramanathan School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India

Sabarathinam Chidambaram Water Research Centre, Kuwait Institute for Scientific Research, Safat, Kuwait

M.P. Jonathan Centro Interdisciplinario de Investigaciones y Estudios sobre Medio Ambiente y Desarrollo (CIIEMAD), Instituto Politécnico Nacional (IPN), Ciudad de México (CDMX), México

M.V. Prasanna Department of Applied Geology, Faculty of Engineering and Science, Curtin University Malaysia, CDT 250, Sarawak, Miri, Malaysia

Pankaj Kumar Natural resources and Ecosystem Services, Institute for Global Environmental Strategies, Hayama, Japan

Francisco Muñoz Arriola Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States; School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE. United States

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-32-385512-9 For Information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals Publisher: Candice Janco Acquisitions Editor: Marisa LaFleur Editorial Project Manager: Leticia M Lima Production Project Manager: Debasish Ghosh Cover Designer: Matthew Limbert Typeset by Aptara, New Delhi, India

Contents Contributors xi Preface xv Acknowledgement xix

I

Environmental modifications, degradation and human health risks 1. COVID-19: a wake-up call to protect planetary health ASH PACHAURI, NORMA PATRICIA MUÑOZ SEVILLA, SHAILLY KEDIA, DRISHYA PATHAK, KOMAL MITTAL, PHILO MAGDALENE A

1.1 Emerging infectious disease, COVID-19, and planetary health 3 1.2 Lockdown as a temporary respite for the environment 4 1.3 Pandemic reclaiming the plastic usage: demand, production, and usage 5 1.4 Waste management: the intensifying crisis 6 1.5 Ocean pollution and landfills 7 1.6 Exacerbated inequalities and vulnerabilities 8 1.7 Recommendations 9 1.8 COVID-19 calls for reflection—conclusion 10 References 12

2. Zoonotic disease in the face of rapidly changing human–nature interactions in the Anthropocene SHAMIK CHAKRABORTY, PANKAJ KUMAR, BINAYA KUMAR MISHRA

2.1 Introduction: why zoonotic diseases can be a concern in the Anthropocene 17 2.2 Resilience and its change due to biodiversity loss and diseases 18 2.3 The case of zoonotic diseases 19 2.4 Possible measures to fight next pandemics with concept of resilience 22 2.5 Conclusion 22 References 23

3. Impact of Covid-19 lockdown on the socioenvironmental scenario of Indian Sundarban PUNARBASU CHAUDHURI, SUBARNA BHATTACHARYYA

3.1 Introduction 25 3.2 Impact on air and water quality 27 3.3 Natural regeneration of biodiversity 30 3.4 Migration of labor from other States 32 3.5 Conclusion 33 References 33

4. Changes in nighttime Lights during COVID-19 lockdown over Delhi, India ASMITA DEEP, PRASUN KUMAR GUPTA

4.1 Introduction 37 4.2 Study area and data used 38 4.3 Methodology 39 4.4 Results and discussion 40 4.5 Conclusions and recommendations 46 Acknowledgements 47 References 47

5. Socio-environmental factors affecting mental health of people during COVID-19 in coastal urban areas of Bangladesh ROZINA AKTER, MUKTA AKTER, MD. TANVIR HOSSAIN, MD. NASIF AHSAN

5.1 Introduction 49 5.2 Method 51 5.3 Results 52 5.4 Conclusion 56 Appendix 1 57 References 60

6. Mitigating transboundary risks by integrating risk reduction frameworks of health and DRR: A perspective from COVID-19 pandemic SIVAPURAM V.R.K. PRABHAKAR, RAJEEV ISSAR, ARPAH BT. ABU BAKAR, MARIKO YOKOO

6.1 Introduction 63 6.2 Impacts of transboundary disasters 64 6.3 Existing risk reduction frameworks and their gaps/ challenges 67 6.4 A comparison of responses to COVID-19 by India and Japan 67 6.5 Measures for strengthening risk reduction frameworks 70 Acknowledgments 74 References 74

vi Contents

II

Water resources: Planning, management and governance 7. An overview of Kuwait’s water resources and a proposed plan to prevent the spread of the Novel Corona Virus (COVID-19) pandemic through Kuwait’s water supply facilities and groundwater system A. AKBER, A. MUKHOPADHYAY

7.1 Prelude 79 7.2 Introduction 79 7.3 Sources of water 80 7.4 Current status of water availability and consumption 81 7.5 Possible spread of the Novel Corona Virus through water facilities 84 7.6 Monitoring of water quality and collection of water samples 85 7.7 Preservation, analysis, and treatment of water samples 86 7.8 Concluding remarks 86 Acknowledgments 86 References 87

8. Survival of SARS-COV-2 in untreated and treated wastewater—a review BANAJARANI PANDA, SABARATHINAM CHIDAMBARAM, ARINDAM MALAKAR

8.1 Introduction 89 8.2 SARS-COV-2 in treated and untreated wastewater 89 8.3 Transmission through wastewater 91 8.4 Impact 92 8.5 Future research needs to be carried out 93 Acknowledgment 93 Conflict of interest 93 References 93

9. Wastewater discharge and surface water contamination pre- and post- COVID 19— global case studies ALOK KUMAR THAKUR, A.L. RAMANATHAN, PROSUN BHATTACHARYA, MANISH KUMAR

9.1 Introduction 95 9.2 Presence in aquatic environment 96 9.3 Persistence and removal 97 9.4 Wastewater-based epidemiology 98 9.5 Case studies 99 9.6 Environmental implications and policies 100 9.7 Conclusion 100 References 101

10. Addressing associated risks of COVID-19 infections across water and wastewater service chain in Asia PHAM NGOC BAO, VU DUC CANH

10.1 Introduction 103 10.2 SARS-CoV-2 in feces and wastewater 104 10.3 Addressing potential risks associated with water and wastewater services 106 10.4 Regular virus surveillance in wastewater for COVID-19 110 10.5 Conclusions and recommendations 111 References 111

11. Governance of wastewater surveillance systems to minimize the impact of COVID-19 and future epidemics: Cases across Asia-Pacific T. TAKEDA, M. KITAJIMA, A. ABEYNAYAKA, N.T.T. HUONG, N.Q. DINH, K. SIRIKANCHANA, M. NAVIA, A.A. SAM, M. TSUDAKA, T. SETIADI, D.T. HUNG, E. HARAMOTO

11.1 State of COVID-19 in selected countries 116 11.2 Wastewater surveillance of COVID-19 117 11.3 Wastewater management in selected countries 117 11.4 Stakeholders for wastewater monitoring 119 11.5 Legislation and frameworks 119 11.6 Challenges and opportunities 121 11.7 Recommendations 121 Acknowledgments 123 References 123

12. Impact of COVID-19 lockdown on real-time DO–BOD variation of river Ganga AJIT KUMAR VIDYARTHI, SUNITI PARASHAR, PRABHAT RANJAN, A.L. RAMANATHAN

12.1 Introduction 127 12.2 Impact of lockdown on main stem of river Ganga 128 12.3 Impact of lockdown on river Ganga tributaries 132 12.4 Conclusion 133 References 133

13. Covid-19 and opportunity for integrated management of water–energy–food resources for urban consumption SHRESTH TAYAL, SWATI SINGH

13.1 Introduction 135 13.2 Methodology 136 13.3 Result and discussion 137 13.4 Integrated mitigation measures 139 13.5 Conclusion 140 References 140

Contents vii

14. COVID-19 lockdown impacts on biochemical and microbiological parameters in southern Indian coast

17. A safe and effective sample collection method for assessment of SARS-CoV-2 in aerosol samples

HENCIYA SANTHASEELAN, VENGATESHWARAN THASU DINAKARAN, SANTHOSH GOKUL MURUGAIAH, MUTHUKUMAR KRISHNAN, ARTHUR JAMES RATHINAM

NAZIMA HABIBI, MONTAHA BEHBEHANI, SAIF UDDIN, FADILA AL-SALAMEEN, ANISHA SHAJAN, FARHANA ZAKIR

14.1 Introduction 143 14.2 Major coastal activities influenced by COVID-19 pandemic 144 14.3 COVID-19 lockdown impacts of biochemical and microbiological parameters on South Indian coasts 145 14.4 Effects of gas emissions with coastal water quality 147 14.5 Refusing on phytoplankton biomass and NO2 emissions 147 14.6 Conclusion 148 References 149

III

Air and water quality: Monitoring, fate, transport, and drivers of socioenvironmental change 15. Air quality index and criteria pollutants in ambient atmosphere over selected sites: Impact and lessons to learn from COVID 19 SUSHIL KUMAR, SUDESH YADAV

15.1 Introduction 153 15.2 Data source and data collection point 155 15.3 Results 157 15.4 Summary 161 Acknowledgments 161 References 161

16. Study of the aerosol parameters and radiative forcing during COVID-19 pandemic over Srinagar Garhwal, Uttarakhand ALOK SAGAR GAUTAM, HARISH CHANDRA NAINWAL, R.S. NEGI, SANJEEV KUMAR, KARAN SINGH

16.1 Introduction 163 16.2 Site description and meteorology 164 16.3 Result and discussions 165 16.4 Conclusions 170 Acknowledgments 170 Abbreviation List 170 References 171

17.1 Introduction 173 17.2 Novel aerosol sampling method 174 17.3. Trizol versus phosphate buffer solution as collection medium 174 17.4 Next generation-based applications 177 17.5 Conclusions 177 References 177

18. Meteorological parameters and COVID-19 spread-Russia a case study SHANKAR K., GNANACHANDRASAMY G., MAHALAKSHMI M., DEVARAJ N., PRASANNA M.V., CHIDAMBARAM S., THILAGAVATHI R.

18.1 Introduction 179 18.2 Study area 180 18.3 Methodology 183 18.4 Results and discussion 183 18.5 Conclusion 187 References 188

19. Short-Term resilience and transformation of urban socioenvironmental systems to COVID-19 lockdowns in India using air quality as proxy JAGRITI JAIN, FRANCISCO MUÑOZ ARRIOLA, DEEPAK KHARE

19.1 Introduction 191 19.2 Area of study and its components 193 19.3 Conceptualization of NAMUSS resilience to COVID-19 194 19.4 Methodology 197 19.5 Results and discussion 198 19.6 Conclusion 204 Acknowledgements 204 References 204

20. Covid-19 Pandemic-changes in the context of global environment and lessons learned NEHA JAISWAL, S. JAYAKUMAR

20.1 Introduction 20.2 The pros and cons of Covid-19 worldwide 20.3 Lessons learned from the current crisis

207 209 217

viii Contents 20.4 Conclusion 218 Abbreviations 219 References 219

IV

Marine and lacustrine environment 21. Coral reefs: globally predicted climate change impact mitigation, mediated by the marine flora and their ecosystem connectivity, with a case study from Neil Island (the Andamans) SIVAKUMAR KANNAN, CHANDANI APPADOO, P. RAGAVAN, BALAJI VEDHARAJAN, GOUTHAM BHARATHI, SIVAPERUMAN CHANDRAKASAN

21.1 Introduction 225 21.2 Mangroves: A refuge for coral reefs in times of climate change 226 21.3 Seagrasses in enhancing reef resilience potential 228 21.4 Reefs–seaweeds interactions in the troubled ocean 229 21.5 Ecosystem connectivity between mangroves, seagrasses and coral reefs 230 21.6 Coastal and marine faunal resources of the Neil Island (the Andamans) - A case study 231 21.7 Fishes 236 21.8 Conclusions 236 Acknowledgement 237 References 237

22. Temporal variability (1966–2020) of the fish assemblage and hydrometeorology of the Tampamachoco Lagoon, Veracruz, Mexico: Pre-and during Covid-19 scenario GUADALUPE M. AUSTRIA-ORTÍZ, ALEJANDRA REYESMÁRQUEZ, EUGENIA LÓPEZ-LÓPEZ, SERGIO AGUÍÑIGAGARCÍAB, JUANA LÓPEZ-MARTÍNEZ

22.1 Introduction 241 22.2 Study area 242 22.3 Methods 243 22.4 Results 245 22.5 Discussion 249 22.6 The COVID-19 pandemic scenario 251 22.7 Conclusion 251 Abbreviations 252 Acknowledgments 252 References 252

23. Socio-economic and environmental impacts of COVID-19 pandemic: Building resilience of the seven lakes of San Pablo city, Philippines DAMASA B. MAGCALE-MACANDOG, CANESIO D. PREDO, JOSEPH G. CAMPANG, JOHN VICENT R. PLETO, MA. GRECHELLE LYN D. PEREZ, NETHANEL JIREH A. LARIDA, FATIMA A. NATUEL, SARENA GRACE L. QUIÑONES, YVES CHRISTIAN L. CABILLON

23.1 Introduction 255 23.2 COVID-19 cases in the Philippines 256 23.3 COVID-19 cases in San Pablo city 257 23.4 Effects of COVID-19 pandemic on the environment 257 23.5 Effects of the pandemic on society and economy 264 23.6 Resilience 266 23.7 Summary and lessons learned 268 Acknowledgement 269 References 269

V

Sustainable development goals and environmental justice 24. Impacts and implications of the COVID-19 crisis and its recovery for achieving sustainable development goals in Asia: A review from an SDG interlinkage perspective XIN ZHOU, MUSTAFA MOINUDDIN

24.1 Introduction 273 24.2 Methodology of the SDG interlinkage analysis 274 24.3 Impacts of COVID-19 on SDGs 276 24.4 Implications of COVID-19 measures for achieving the SDGs: A review from an SDG interlinkage perspective 281 24.5 Discussion 285 24.6 Conclusion 286 Acknowledgements 287 References 287

25. The COVID-19 impacts on India’s low carbon infrastructure NANDAKUMAR JANARDHANAN, RITIKA MANDHYAN, ATUL RAWAT, ERI IKEDA

25.1 Introduction 25.2 Impact on renewable energy infrastructure

289 290

Contents ix

25.3 Challenges to the deveopment of low carbon infrastructure and smart cities 291 25.4 Responses towards the impact on low carbon infrastructure: policy analysis 293 25.5 Conclusion 295 References 295

26. Green spaces resume their importance in cities after the COVID-19 pandemic: A case of study from Mexico City MARÍA CONCEPCIÓN MARTÍNEZ RODRÍGUEZ, ANA LAURA CERVANTES NÁJERA, MARTÍN VERA MARTÍNEZ

26.1 Introduction 299 26.2 Cities as epicentres for the spread of the coronavirus 301 26.3 Agenda 2030 and the sustainable development goals 303 26.4 Mexico City: A case study 304 26.5 Reflections 307 26.6 Conclusions 308 Acknowledgement 308 References 308

27. Climate change, adaptation and gender concerns: Approaches and learnings from global and Indian experiences VIJETA RATTANI, SOMYA BHATT, DEEPAK SINGH

27.1 Introduction 311 27.2 Gender differentiated impacts of climate change 312 27.3 The state of gender representation in global climate agenda 313 27.4 The global gender agenda 314 27.5 Adding a gender perspective to climate actions 315 27.6 Recognition of gender considerations in climate actions in India 316 27.7 Approaches and learnings from India 317 27.8 Results/outcomes 319 27.9 Key learnings and conclusion 320 References 321

28. Urban housing in the metropolitan area of the Mexico City, in the context of climate change and the COVID 19 pandemic JUAN MAYORGA, JOSÉ SOTO

28.1 Introduction 28.2 Climate change in the world, origins of its study 28.3 Climate change in Mexico in the 21st century 28.4 Mexico’s urban areas in the 21st Century 28.5 COVID-19 pandemic in Mexico and the world

323 323 324 325 325

28.6 The study of urban housing in Mexico during the COVID-19 pandemic 326 28.7 Characterization of the surveyed subjects living in the urban dwellings studied 327 28.8 Characteristics of urban dwellings registered in the survey 327 28.9 Problems in the urban areas of the metropolitan area of Mexico City during and post COVID-19 pandemic 328 28.10 Recommendations for the sustainable and resilient design of the urban spaces studied 328 28.11 Housing problems in urban areas of the metropolitan area of Mexico City studied 329 28.12 Recommendations for urban dwellings in the metropolitan area of Mexico City studied 331 28.13 Conclusions 331 References 332

29. COVID-19 as an opportunity to make fieldbased earth sciences and other similar courses easily accessible and affordable AAISYAH D., SAHARI S., SHAH A.A., QADIR A., PRASANNA M.V., SHALABY R.

29.1 Introduction 333 29.2 Background 334 29.3 Materials and methods 335 29.4 Results and interpretations 337 29.5 Discussion 340 29.6 Conclusions 341 Acknowledgement 341 References 341

30. Livelihood and health vulnerabilities of forest resource-dependent communities amidst the COVID-19 pandemic in southwestern regions of Bangladesh TAPOSHI RABYA LIMA, MAHFUZA ZAMAN ELA, LUBABA KHAN, TAUFIQ-E-AHMED SHOVO, MD. TANVIR HOSSAIN, NUSRAT JAHAN, KHANDKAR-SIDDIKUR RAHMAN, MD. NASIF AHSAN, MD. NAZRUL ISLAM

30.1 Introduction 343 30.2 COVID-19 pandemic situation and its impacts on forest resource-dependent communities 344 30.3 The Sundarbans forest of Bangladesh and the resource-dependent communities 345 30.4 Materials and methods 346 30.5 Impact assessment of COVID-19 on the Sundarbans forest-dependent communities 348

x Contents 30.6 Coping strategies of the Sundarbans forest-dependent communities in the pandemic situation 351 30.7 Conclusion and recommendations 352 30.8 Appendix 353 References 354

31. Sustainable utilization of natural resources for socio-environmental resilience and transformation in the mountains of Nepal ARJUN GAUTAM, RAVI BHANDARI, BINAYA KUMAR MISHRA, BASANTA BARAL

31.1 Natural resources and environment: background 357 31.2 Local and indigenous practices 358 31.3 Policies, programs, and institutions 360 31.4 COVID-19 and changing scenario on mountain economy 362 31.5 Resilience and transformation through sustainable utilization 364 31.6 Summary 369 References 370

32. How resilient are mountain livelihoods against extreme events? Learnings from Central Mexico in a COVID-19 world BARBARA KOVÁCS, JUAN CARLOS CAMPOS BENHUMEA

32.1 Introduction 32.2 Methods and material 32.3 Results and discussion 32.4 Conclusions 32.5 Credit author statement

373 374 377 381 381

32.6 Declaration of competing interest 381 Acknowledgement 381 Abbreviations 381 References 382

33. Significance of conventional Indian food acting as immune boosters to overcome COVID-19 MADHAVI LATHA KONE, DHANU RADHA SAMAYAMANTHULA

33.1 Introduction 385 33.2 Methodology 386 33.3 Results and discussion 386 33.4 Conclusion 394 References 394

34. COVID-19 pandemic impact on food security and food system of India: Lessons for future USHA MINA, RAM KUMAR

34.1 Introduction 397 34.2 COVID pandemic: Food security and food system of India 399 34.3 COVID pandemic impact on food system productive attribute of India 399 34.4 COVID pandemic and food security in India 403 34.5 COVID pandemic and future lessons 404 34.6 Conclusion 405 Acknowledgements 406 References 406

Index 409

Contributors

A.A. Sam  Department of Social Studies, Faculty of Arts, National University of Samoa, Lepapaigalagala, Toomatagi, Samoa A.A. Shah  Department of Geosciences, Universiti Brunei Darussalam, Brunei AL. Ramanathan  School of Environmental Sciences, Jawaharlal Nehru University, New Delhi 110067, India A. Abeynayaka  Graduate School of Environment and Information Studies, Tokyo City University, Yokohama 158-0087, Japan; Research and Development Division, Pirika Inc./Pirika Association, Ebisu, Shibuya City, Tokyo 150-0013, Japan A. Akber  Water Research Center, Kuwait Institute for Scientific Research, Kuwait A. Mukhopadhyay  Water Research Center, Kuwait Institute for Scientific Research, Kuwait A. Qadir  Independent Researcher Ajit Kumar Vidyarthi  Central Pollution Control Board, New Delhi, India Alejandra Reyes-Márquez  Instituto Politécnico Nacional, Escuela Nacional de Ciencias Biológicas, Prol. del Carpio y Plan de Ayala s/n Col. Santo Tomás, CDMX, México Alok Kumar Thakur  Discipline of Earth Science, Indian Institute of Technology Gandhinagar, Gujarat 382 355, India Alok Sagar Gautam  Department of Physics, HNB Garhwal University, Srinagar Garhwal, Uttarakhand, India

Asmita Deep  Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO), Dehradun, India; Faculty of Geoinformation Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands Atul Rawat  University of Petroleum and Energy Studies, Derhadun, India Balaji Vedharajan  OMCAR Palk Bay Centre, East Coast Road, Thanjavur District, Tamil Nadu, India Banajarani Panda  Water Sciences Lab, University of Nebraska-Lincoln, Lincoln, Nebraska, USA Barbara Kovács  Centro Interdisciplinario de Investigaciones y Estudios sobre Medio Ambiente y Desarrollo (CIIEMAD), Instituto Politécnico Nacional (IPN), Calle 30 de Junio de 1520 s/n, Barrio La Laguna Ticomán, Del. Gustavo A. Madero, C.P., Ciudad de México (CDMX), Mexico Basanta Baral  DV Excellus Pvt. Ltd., Nepal Binaya Kumar Mishra  School of Engineering, Pokhara University, Nepal Canesio D. Predo  Institute of Renewable Natural Resources, University of the Philippines Los Baños, College, Laguna, Philippines Chandani Appadoo  Department of Biosciences and Ocean Studies, University of Mauritius, Reduit, Mauritius D.T. Hung  Laboratory Center, Hanoi University of Public Health, 1A Duc Thang Road, Duc Thang Ward, North Tu Liem District, Hanoi, Viet Nam

Ana Laura Cervantes Nájera  Centro Interdisciplinario de Investigaciones y Estudios sobre Medio Ambiente y Desarrollo (CIIEMAD) from Instituto Politécnico Nacional (IPN), 30 deJunio de 1520, La Laguna Ticomán, Gustavo A. Madero, Mexico City

D. Aaisyah  Department of Geosciences, Universiti Brunei Darussalam, Brunei

Anisha Shajan  Biotechnology Program, Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Kuwait

Deepak Khare  Department of Water Resources Development and Management, Indian Institute of Technology, Roorkee, India

Arindam Malakar  Water Sciences Lab, University of Nebraska-Lincoln, Lincoln, Nebraska, USA

Deepak Singh  Department of Geography and Resource Management, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong, SAR, China

Arjun Gautam  School of Engineering, Pokhara University, Nepal

Damasa B. Magcale-Macandog  Institute of Biological Sciences, University of the Philippines Los Baños, College, Laguna, Philippines

Arpah bt. Abu Bakar  Universiti Utara Malaysia, Kedah, Malaysia

Dhanu Radha Samayamanthula  Water Research Center, Kuwait Institute for Scientific Research, Shuwaikh, Kuwait

Arthur James Rathinam  Department of Marine Science, Bharathidasan University, Tiruchirappalli 620024, India

Drishya Pathak  POP (Protect Our Planet) Movement, 800 Third Avenue, Suite 2800, New York, NY 10022, USA

Ash Pachauri  POP (Protect Our Planet) Movement, 800 Third Avenue, Suite 2800, New York, NY 10022, USA

E. Haramoto  Interdisciplinary Center for River Basin Environment, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan

xii Contributors Eri Ikeda  Indian Institute of Technology Delhi, India Eugenia López-López  Instituto Politécnico Nacional, Escuela Nacional de Ciencias Biológicas, Prol. del Carpio y Plan de Ayala s/n Col. Santo Tomás, CDMX, México Fadila Al-Salameen  Biotechnology Program, Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Kuwait Farhana Zakir  Food and Nutrition Program, Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Kuwait Fatima A. Natuel  School of Environmental Science and Management, University of the Philippines Los Baños, College, Laguna, Philippines Francisco Muñoz Arriola  Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States; School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE. United States G. Gnanachandrasamy  School of Geography and Planning, Sun Yat -Sen University, Guangzhou, P.R. China; Center for Earth, Environment and Resources, Sun Yat -Sen University, Guangzhou, P.R. China Goutham Bharathi  Andaman & Nicobar Regional Centre, Zoological Survey of India, Port Blair, Andaman & Nicobar Islands, India Guadalupe M. Austria-Ortíz  Instituto Politécnico Nacional, Escuela Nacional de Ciencias Biológicas, Prol. del Carpio y Plan de Ayala s/n Col. Santo Tomás, CDMX, México Harish Chandra Nainwal  Department of Geology, HNB Garhwal University, Srinagar Garhwal, Uttarakhand, India Henciya Santhaseelan  Department of Marine Science, Bharathidasan University, Tiruchirappalli 620024, India Jagriti Jain  Department of Water Resources Development and Management, Indian Institute of Technology, Roorkee, India John Vicent R. Pleto  Institute of Biological Sciences, University of the Philippines Los Baños, College, Laguna, Philippines Joseph G. Campang  Institute of Biological Sciences, University of the Philippines Los Baños, College, Laguna, Philippines José Soto  ESIA Tecamachalco Campus, Instituto Politécnico Nacional (National Polytechnic Institute), Fuente de los Leones avenue 28, Lomas de Tecamachalco, Estado de México, México Juana López-Martínez  Centro de Investigaciones Biológicas del Noroeste, Km. 1 Carretera a San Juan de La Costa “EL COMITAN” La Paz, BCS, México Juan Carlos Campos Benhumea  Centro Interdisciplinario de Investigaciones y Estudios sobre Medio Ambiente y Desarrollo (CIIEMAD), Instituto Politécnico Nacional (IPN), Calle 30 de Junio de 1520 s/n, Barrio La Laguna Ticomán, Del. Gustavo A. Madero, C.P., Ciudad de México (CDMX), Mexico Juan Mayorga  ESIA Tecamachalco Campus, Instituto Politécnico Nacional (National Polytechnic Institute), Fuente de los

Leones avenue 28, Lomas de Tecamachalco, Estado de México, México K. Shankar  Department of Applied Geology, School of Applied Natural Science, Adama Science and Technology University, Adama, Ethiopia K. Sirikanchana  Research Laboratory of Biotechnology, Chulabhorn Research Institute, 54 Kampangpetch 6 Road Laksi, Bangkok 10210, Thailand Karan Singh  Department of Physics, HNB Garhwal University, Srinagar Garhwal, Uttarakhand, India Khandkar-Siddikur Rahman  Solidaridad Network Asia, Dhaka, Bangladesh Komal Mittal  POP (Protect Our Planet) Movement, 800 Third Avenue, Suite 2800, New York, NY 10022, USA Lubaba Khan  Sociology Discipline, Social Science School, Khulna University, Khulna, Bangladesh M.V. Prasanna  Department of Applied Geology, Faculty of Engineering and Science, Curtin University Malaysia, CDT 250, Sarawak, Miri, Malaysia M. Kitajima  Division of Environmental Engineering, Hokkaido University, North 13 West 8 Kita-ku, Sapporo, Hokkaido 060-8628, Japan M. Mahalakshmi  School of Civil Engineering, SASTRA Deemed University, Thanjavur, India M. Navia  Thunderstruck Resources Limited, Nadi Back Road, Nadi, Fiji M. Tsudaka  Strategic Management Office, Institute for Global Environmental Strategies, 2108-11 Kamiyamaguchi, Hayama, Kanagawa 240-0115, Japan Ma. Grechelle Lyn D. Perez  School of Environmental Science and Management, University of the Philippines Los Baños, College, Laguna, Philippines Madhavi Latha Kone  Department of Gynecology, Kalyan Hospital,Payakoropeta, Andhra Pradesh, India Mahfuza Zaman Ela  Sociology Discipline, Social Science School, Khulna University, Khulna, Bangladesh Manish Kumar  Discipline of Earth Science, Indian Institute of Technology Gandhinagar, Gujarat 382 355, India Mariko Yokoo  Institute for Global Environmental Strategies (IGES), Hayama, Japan Martín Vera Martínez  Universidad Autónoma de Baja California. Tijuana, Universidad 14418, Parque Internacional Industrial Tijuana, Baja California, Tijuana, Mexico María Concepción Martínez Rodríguez  Centro Interdisciplinario de Investigaciones y Estudios sobre Medio Ambiente y Desarrollo (CIIEMAD) from Instituto Politécnico Nacional (IPN), 30 deJunio de 1520, La Laguna Ticomán, Gustavo A. Madero, Mexico City Md. Nasif Ahsan  Economics Discipline, Social Science School, Khulna University 9208, Bangladesh Md. Nasif Ahsan  Economics Discipline, Social Science School, Khulna University, Khulna, Bangladesh Md. Nazrul Islam  Forestry and Wood Technology Discipline, Life Science School, Khulna University, Khulna, Bangladesh

Contributors xiii

Md. Tanvir Hossain  Sociology Discipline, Social Science School, Khulna University 9208, Bangladesh

Punarbasu Chaudhuri  Department of Environmental Science, University of Calcutta, Kolkata, India

Md. Tanvir Hossain  Sociology Discipline, Social Science School, Khulna University, Khulna, Bangladesh

R.S. Negi  Department of Rural Technology, HNB Garhwal University, Srinagar Garhwal, Uttarakhand, India

Montaha Behbehani  Environment Pollution and Climate Change Program, Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Kuwait

R. Shalaby  Department of Geosciences, Universiti Brunei Darussalam, Brunei

Mukta Akter  Economics Discipline, Social Science School, Khulna University 9208, Bangladesh Mustafa Moinuddin  Institute for Global Environmental Strategies (IGES), 2108-11Kamiyamaguchi, Hayama, Kanagawa, Japan Muthukumar Krishnan  Department of Physics, National Institute of Technology, Tiruchirappalli 620015, India N.Q. Dinh  Vietnam Institute of Geosciences and Mineral Resources (VIGMR), 67 Chien-Thang-Str, Thanh Xuan, 100000 Hanoi, Viet Nam N.T.T. Huong  Faculty of Biotechnology, Chemistry and Environmental Engineering, Phenikaa University, Hanoi 100000, Viet Nam; Phenikaa Research and Technology Institute (PRATI), A&A Green Phoenix Group, 167 Hoang Ngan, Hanoi 10000, Viet Nam N. Devaraj  Department of Earth Sciences, Annamalai University, Annamalai Nagar, Tamilnadu, India Nandakumar Janardhanan  Institute for Global Environmental Strategies, Japan Nazima Habibi  Biotechnology Program, Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Kuwait Neha Jaiswal  Dept. of Ecology and Environmental Sciences, School of Life Sciences, Pondicherry University, Puducherry Nethanel Jireh A. Larida  Institute of Biological Sciences, University of the Philippines Los Baños, College, Laguna, Philippines Norma Patricia Muñoz Sevilla  Instituto Politécnico Nacional, CIIEMAD, POP Movement, Calle 30 de Junio de 1520 s/n, Colonia Barrio de la Laguna, Ticomán, CDMX 07340, Mexico City, Mexico Nusrat Jahan  Sociology Discipline, Social Science School, Khulna University, Khulna, Bangladesh P. Ragavan  Physical Research Laboratory, Ahmedabad, India Pankaj Kumar  Natural resources and Ecosystem Services, Institute for Global Environmental Strategies, Hayama, Japan Pham Ngoc Bao  Natural Resources and Ecosystem Services, Institute for Global Environmental Strategies, Kanagawa, Japan Philo Magdalene A  POP (Protect Our Planet) Movement, 800 Third Avenue, Suite 2800, New York, NY 10022, USA Prabhat Ranjan  Central Pollution Control Board, New Delhi, India Prasun Kumar Gupta  Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO), Dehradun, India Prosun Bhattacharya  Department of Sustainable Development, Environmental Science and Engineering, KTH Royal Institute of Technology, Teknikringen 10B, Stockholm SE-10044, Sweden

R. Thilagavathi  Department of Earth Sciences, Annamalai University, Annamalai Nagar, Tamilnadu, India Rajeev Issar  UNDP, Bangkok, Thailand Ram Kumar  Directorate of Agriculture, New Secretariat, Bihar, India Ravi Bhandari  Nepal Innovation Technology & Entrepreneurship Center, Pokhara University, Nepal Ritika Mandhyan  Department of Urban Engineering, University of Tokyo, Japan Rozina Akter  Economics Discipline, Social Science School, Khulna University 9208, Bangladesh Sabarathinam Chidambaram  Water Research Centre, Kuwait Institute for Scientific Research, Safat, Kuwait S. Jayakumar  Dept. of Ecology and Environmental Sciences, School of Life Sciences, Pondicherry University, Puducherry S. Sahari  Department of Geosciences, Universiti Brunei Darussalam, Brunei Saif Uddin  Environment Pollution and Climate Change Program, Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Kuwait Sanjeev Kumar  Department of Physics, HNB Garhwal University, Srinagar Garhwal, Uttarakhand, India Santhosh Gokul Murugaiah  Department of Marine Science, Bharathidasan University, Tiruchirappalli 620024, India Sarena Grace L. Quiñones  Institute of Biological Sciences, University of the Philippines Los Baños, College, Laguna, Philippines Sergio Aguíñiga-Garcíab  Instituto Politécnico Nacional, Centro Interdisciplinario de Ciencias Marinas, Avenida Politécnico Nacional s/n Col. Playa Palo de Santa Rita, Apartado Postal 592, La Paz, BCS, México Shailly Kedia  The Energy & Resources Institute, Jawaharlal Nehru University, New Delhi, India Shamik Chakraborty  Hosei University Tokyo, Japan Shresth Tayal  The Energy and Resources Institute, New Delhi, India Sivakumar Kannan  CAS in Marine Biology, Faculty of Marine Sciences, Annamalai University, TN, India Sivaperuman Chandrakasan  Andaman & Nicobar Regional Centre, Zoological Survey of India, Port Blair, Andaman & Nicobar Islands, India Sivapuram V.R.K. Prabhakar  Institute for Global Environmental Strategies (IGES), Hayama, Japan Somya Bhatt  Environment, Climate Change and Natural Resource Management Programme, Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, New Delhi, India

xiv Contributors Subarna Bhattacharyya  School of Environmental Studies, Jadavpur University, Jadavpur, India Sudesh Yadav  School of Environmental Sciences, Jawaharlal Nehru University, New Delhi 110067, India Suniti Parashar  Central Pollution Control Board, New Delhi, India Sushil Kumar  School of Environmental Sciences, Jawaharlal Nehru University, New Delhi 110067, India Swati Singh  TERI School of Advanced Studies, New Delhi, India T. Setiadi  Centre for Environmental Studies (PSLH), Institut Teknologi Bandung, Jl. Sangkuriang 42 A, Bandung 40135, Indonesia T. Takeda  Department of Earth and Planetary Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan Taposhi Rabya Lima  Sociology Discipline, Social Science School, Khulna University, Khulna, Bangladesh

Taufiq-E-Ahmed Shovo  Sociology Discipline, Social Science School, Khulna University, Khulna, Bangladesh; School of Humanities and Social Science, Faculty of Education and Arts, University of Newcastle, Callaghan, NSW, Australia Usha Mina  School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India Vengateshwaran Thasu Dinakaran  Department of Marine Science, Bharathidasan University, Tiruchirappalli 620024, India Vijeta Rattani  Environment, Climate Change and Natural Resource Management Programme, Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, New Delhi, India Vu Duc Canh  Department of Urban Engineering, the University of Tokyo, Tokyo, Japan Xin Zhou  Institute for Global Environmental Strategies (IGES), 2108-11Kamiyamaguchi, Hayama, Kanagawa, Japan Yves Christian L. Cabillon  Institute of Biological Sciences, University of the Philippines Los Baños, College, Laguna, Philippines

Preface

The whole world has been on intermittent lockdowns due to the present COVID-19 pandemic. The industrial deacceleration caused by the pandemic has affected everyone without distinction of wealth, religion, or nationality. Also, the pandemic has evidenced how socio-environmental processes have been altered or accentuated, relieving the environment momentarily from an almost chronic deterioration. Likewise, the lockdown has affected the aquatic systems where direct or indirect improvements in ecosystem services have been observed. In the atmosphere, the presence and dynamics of pollutants worldwide provide some insights into the responsibility of the acceleration of the human enterprise. The induced de-acceleration, evidenced in reduced human movement, industrial closure, and tourists’ reduction, also affected water quality, fish populations in lacustrine, marine, and lagoon environments. This pandemic has opened up new challenges focusing on sustainable development goals in the global economy and the way forward to achieve them on time. The book presents five different themes, with thirtyfour chapters capturing the collective thinking on how our environment change during the COVID-19 lockdowns. The studies encompass short-term variations of specific climatic factors that indicate a shred of unclear evidence on climate change mitigation. Yet, the collective evidence represents the basis for assessing how a sudden reduction in our industrialization can benefit the environment. As humankind suffers the most significant threat since World War II, the COVID-19 has made us aware of our individual and global interactions with the environment. We have seen how socially and economically vulnerable we are to COVID-19 by confining individuals and communities to tackle the pandemic and using social networks and media to survive social distancing and quarantine. We have seen how persistent inequality makes chronically disadvantaged communities more vulnerable to a virus that has attacked our bodies’ and societal functionalities. Across the globe and within small communities, the pandemic has evidenced educational, technological, and social divides leading to uneven access to safe jobs, affordable health care, and educational services. The present book, “Environmental Resilience And Transformation in Times of COVID-19,” integrates a collective of analyses and assessments on how COVID-19

lockdowns have influenced the socioenvironmental processes amid climate change. Sevilla et al., provide a global perspective of how our natural and built systems have been altered with polarized ramifications. For example, the short-term reclaims of flora and fauna for spaces and the estimated long-term effect of plastic disposal due to the increase in packaging and PPE use, both activated by our incipient understanding and handling of the pandemic. Also, both situations evidence how social and environmental drivers of our Earth-living support system are interwoven. Chakraborty et al., propose that the expansion of zoonotic diseases and weakened resilience resides in increasing exposure to the natural environment through our more invasive management of natural ecosystems. Chaudhuri and Bhattacharyya illustrate how this coupling increases the chances of the spreading of new viruses to humans, integrating socioenvironmental elements of human health attributions to environmental shifts. As the pandemic evolves, the pandemic evidences novel or unexpected expressions of interdependence between the human-environment. Deep and Gupta use night-time lights to identify the relationship between energy consumption and human activity in Delhi, India. Typical regimes shifted during the lockdown to decreased energy consumption and an increase in night-time lights due to workers’ temporal return to their hometowns. Such socioenvironmental expressions in urban centers are also expressed in human behavior, like those identified by Ahsan et al. They highlighted how human stress and anxiety in Bangladesh’s coastal urban areas emerge. The symptoms of stress and anxiety in populations of developing countries triggered by agitation, scarcity, trauma, infodemic, age, literacy level, and living condition can indicate a deterioration of socioenvironmental systems’ resilience abilities. Changes in the functionalities within a community and across boundaries can lead to increased community risks because of the impacts of health emergencies or natural disasters as mentioned by Prabhakar et al. Environmental (water and air) quality integrated the social and environmental responses to the COVID-19 lockdowns. To start, water quality in the book provides a glance of methodologies to track COVID-19 and evaluate environmental states during the lockdown phases. The COVID-19 pandemic forced several nations to impose

xvi Preface restrictions on all activities including industrial and vehicular movements. It is expected to reduce the rate of pollutants entering the ecosystem in many places. Since water pollution remained a major concern over a few decades before COVID, these studies analyze the impact of lockdown on water quality to get an insight into the short-term environmental changes. Akbar et al highlight the spread of COVID through water facilities and the GW system in Kuwait. It addresses sample collection, preservation, and analysis and identifies the migration pathways for possible treatment to remove viruses in contaminated mediums. The SARS-CoV-2 survival in treated and untreated water generated by infected humans. Panda et al, Manish et al, Bao and Canh, and Takeda et al, recommends adopting 100% removal treatment to stop its spread in high population density areas to minimize the transmission of the lethal virus. The fate of water and wastewater contamination of COVID RNA in various countries was brought forward here. It stresses the framework for epidemiology management and proper surveillance of wastewater to avoid fecal/ urinal shedding of infected individuals. Effective monitoring in infected communities at an early-stage through wastewater-based epidemiology, together with clinical diagnostic testing or clinical surveillance is poor, effective interventions, and preparedness actions can be taken as early as possible to restrict the movements of the infected population, as well as to minimize the pathogen spread and threat to public health. To get an insight into the existing challenges and bottlenecks, a comparative study between eight countries across the Asia-Pacific was carried out in this chapter. A better understanding of common issues, as well as issues specific to each country, makes it is possible to build a robust multistakeholder system to monitor SARS-CoV-2 as well as future pathogens in wastewater as an effective disease surveillance system for COVID-19 and unknown epidemics (disease X) in the community level. Ranjan listed first-hand information on the COVID lockdown period from the most densely populated river in the world: Ganga. Mainstream recorded much-improved water quality levels with respect to DO and BOD at most locations during the lockdown as a cumulative effect of rains and decreased industrial & commercial activities. Tayal and Singh, discussed the various aspects of the water-energyfood (WEF) nexus in two cities in India under pre- and post-COVID scenario for implementing integrated measures to ensure optimization of WEF for sustainable environmental benefits. James et al, brought out the impact of waste-water input on coastal water quality due to COVID lockdown, which was found to have a considerable benefit on the coastal ecosystem in the Gulf of Manner with a reduced concentration in heavy metals,

microplastics, NO3, and Chl-a indicating phytoplankton biomass enhancement. As the world came to a standstill there were multiple changes in the atmospheric environment with reductions in major air pollutants, which were used in in many places in multiple industrial and transportation sectors. Air pollution reductions, ozone increases, and manifold changes in the atmospheric conditions of major cities around the world are some of the important variations around the globe. All these changes have not only shown or modified due to the pandemia but the large scale variations have also heavily affected the environment. Kumar and Yadav, provided an input on the changes in air quality index and the pollutants in the ambient atmosphere in New Delhi, India. The four phase lockdown in New Delhi (from 25th March to 31st May, 2020) indicates reduction of CO, NOx, SO2, Pb, O3, and PM2.5 was not on the same level to all pollutants. Aerosol parameters were measured during the lockdown period in Garhwal University of Srinagar, India. The higher values of aerosols observed during the lockdown period is mainly due to the transportation of aerosols and may be due to the precipitation/ washout of aerosols (Gautam et al). Habibi et al, demonstrated a safe way to collect airborne samples of COVID-19 virus through high precaution. The results suggest that samples collected with TRIzol gave 90%-100% of the microbial load, which is highly safe, meeting all the requirements of the virology laboratory. Shankar et al, reported the importance of studying the meteorological parameters and the COVID19 spread in Russia. Overall results collected from 31st January to 31st August, 2020 indicate positive relations with COVID cases and temperature rise and vice versa. Moreover, identifying these changes will help the public and policy makers in the near future. A short-term resilience study was done in Mumbai, India during the lockdown period to understand the changes in air quality and its indices. The results indicate urban socioenvironmental systems with particular indices were identified for environmental and social aspects which act as a social drive of resilience (Jain et al). A global study was done on the environmental changes and the lessons learned during the COVID-19 pandemic. The chapter focuses on the past climatic issues across the globe and how the environment is capable of adapting and its transformation during this period, where various lessons are taught to protect our environment from the pandemic (Jaiswal and Jeyakumar). The aquatic environment, characterized in the collective presented in this book, integrates three studies in a land-to-ocean assessment of the impact of the COVID-19 lockdowns. Theme 4 focuses on the impact of the pandemic on the marine, lagoonal, and lacustrine environment. The Marine environment has served as the key for the survival of corals, dependent biota, and plays a

Preface xvii

significant role in the food chain. The lagoonal environment has been a sink for many contaminants and attempts to filter the anthropogenic stress reaching the marine ecosystem. The freshwater ecosystem in the recent days are affected by tourism, inland sewage, industrial waste, etc., affecting the biota. The observations made by Sivakumar et al have also revealed that the marine flora and the reef ecosystem had a significant impact during this pandemic period. The chapter also highlights that the mangrove habitats can act as alternate refuges for corals during climate threats, particularly increasing seawater temperature, high levels of solar radiation, and ocean acidification. Guadalupe et al, have also recorded that the COVID-19 has registered an incipient evidence of trophic recovery in the Tampamachoco lagoonal environment. It is emphasized that the interconnectivity of the marine ecosystems should be considered for deriving the management policies to protect the ecological health of these coastal habitats. The fresh water bodies, such as lakes and lagoons, have also suffered socioeconomic and environmental impacts. Damasa et al has identified the improvement in water quality and increase in fish population after the lockdown period. This study has provided a sign of positive development in the environmental condition due to the Bayanihan Acts of Philippine government to fight COVID-19. The change observed was mainly due to reduction in the tourist and anthropogenic activities. Sustainable Development Goals (SDGs) and environmental justice in the book highlights the slow progress for achieving different global goals especially those in the world’s developing economies and what are the additional challenges and opportunities imposed by COVID-19 pandemic to achieve them in a timely manner. Finally, it discusses the way forward for recovery, the need to redesign our system-oriented policies to be more inclusive with constricted interventions in order to achieve mutually agreed global goals like Sustainable Development Goals (SDGs), Aichi Target, Sendai Framework for Disaster Management, etc., a necessary ingredient for achieving sustainable development, economic growth, and human well-being in timely manner. The area of thrust of all eleven chapters within this theme ranges from sectors like energy, natural resources, and food security. They all echoed for the need to codesign and coimplement the environmental adaptation and mitigation strategies to preserve our natural resources, boost the immune system and make our system more

robust in nature to absorb sudden additional shocks likes COVID-19 pandemics along with rapid global changes and uncertain extreme weather conditions. Zhou and Moinuddin, highlighted that due to the intrinsic interactions among the SDGs, an interlinkage perspective is very important to grasp a wider picture of COVID-19 impact and inform about the synergies and trade-offs of the COVID-19 measures in order to achieve SDGs by year 2030. They have proposed SDG interlinkage analysis for identifying the impacts of COVID-19 and its recovery and then applied for an empirical study for two Asian countries, Bangladesh and the Republic of Korea. Janardhanan et al, discuss creating or promoting decarbonized and decentralized society with both economic and social benefits for India’s low carbon infrastructure. Rodriguez et al, looks into lessons learned through these cascade effects of COVID-19 pandemic, and promotes greener economies for enhancing system resilience and adaptive capacity. Rattani et al, presents how COVID-19 brought additional and disproportional challenges for women and girls, the weaker section of society in different parts of India. It also presents different approaches for analyzing gender specific differentiated risk assessment and finally highlights the importance of state of art of gender consideration in national policies as a sustainable solution. Cervantes and Ruíz, and Shah, have presented recommendations that can serve in the development of methods for the diagnosis of existing urban houses and newly created dwelling and thereby make them sustainable and resilient. Three different chapters (Hossain et al, Bhandari et al, and Kovacs and Benhumea) highlight unseen problems and challenges COVID-19 brought to the people or communities dependent on natural resources, such as mountains and forests for their livelihood. They also discussed how community resilience in these vulnerable areas can be built through the reduction of consumption, diversification of income sources, and claiming support from the government. Two chapters (Samayamanthula and Latha, Mina and Kumar) have discussed various challenges different countries are facing to achieve food security as a most pressing issue in the time of this uncertain pandemic. They highlighted the importance of immunity and how to build up an immune system to fight against any viral infections especially COVID-19 by adopting conventional foods habits. It also stressed the resilient food system-based policy interventions needs to be implemented by policy makers.

Acknowledgements

We would like to thank all the contributing authors who, during the pandemic, provided scientific narratives on diverse themesand critical findings and shared their knowledge on our environment. We appreciate the two external reviewers for each chapter around the globe who contributed improving the quality of the book. We would like to thank the special work of the Elsevier publishing teams and especially Editorial Project Leader Ms. Leticia Lima. We sincerely thank all six institutions from different countries: 1) School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India; 2) Water Research

 

 

 

Centre, Kuwait Institute for Scientific Research, Kuwait; 3) Center for Interdisciplinary Studies on Environment and Development (Centro Interdisciplinario de Investigaciones y Estudios sobre Medio Ambiente y Desarrollo), National Polytechnic Institute  (Instituto Politécnico Nacional), Mexico City, Mexico; 4) Faculty of Engineering and Science, Department of Applied Geology, Curtin University Malaysia, Malaysia; 5) Natural Resources and Ecosystem Services, Institute for Global Environmental Strategies (IGES), Japan; and 6) Department of Biological Systems Engineering, School of Natural Resources, University of Nebraska-Lincoln, USA.

 

 

P A R T

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Environmental modifications, degradation and human health risks 1  COVID-19: a wake-up call to protect planetary health 2  Zoonotic disease in the face of rapidly changing human-nature interactions in the Anthropocene 3  Impact of Covid-19 lockdown on the socioenvironmental scenario of Indian Sundarban 4  Changes in nighttime Lights during COVID-19 lockdown over Delhi, India 5  Socio-environmental factors affecting mental health of people during COVID-19 in coastal urban areas of Bangladesh 6  Mitigating transboundary risks by integrating risk reduction frameworks of health and DRR: A perspective from COVID-19 pandemic

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C H A P T E R

1 COVID-19: a wake-up call to protect planetary health Ash Pachauria, Norma Patricia Muñoz Sevillab, Shailly Kediac, Drishya Pathaka, Komal Mittala, Philo Magdalene Aa a

POP (Protect Our Planet) Movement, 800 Third Avenue, Suite 2800, New York, NY 10022, USA b Instituto Politécnico Nacional, CIIEMAD, POP Movement, Calle 30 de Junio de 1520 s/n, Colonia Barrio de la Laguna, Ticomán, CDMX 07340, Mexico City, Mexico c The Energy & Resources Institute, Jawaharlal Nehru University, New Delhi, India

1.1  Emerging infectious disease, COVID-19, and planetary health While human health research scarcely considers the surrounding natural ecosystems, a relatively new discipline, planetary health, examines the health of human civilization along with the state of the natural systems on which it depends (Vidal, 2020a; Horton et  al., 2014). The field of planetary health is gaining attention, as the connections between human well-being and ecosystem health become increasingly evident. Infectious outbreaks, like the novel coronavirus, threaten to become more common as human populations destroy habitats, forcing wildlife into closer proximity to humans (Johnson, 2020). The US Centers for Disease Control and Prevention estimates that threequarters of new or emerging diseases that infect humans originate in animals, with research suggesting that outbreaks of infectious diseases such as Ebola, SARS, MERS, bird flu, and now Coronavirus disease 2019 (COVID-19) are on the rise (Vidal, 2020b; Smith et  al., 2014). As humans continue to encroach on animal habitat and destroy fragile ecosystems, they come into ever greater contact with animals. In addition, illegal wildlife trade and illegal live animal markets are frequent causes of such diseases. COVID-19 is the latest infectious disease of likely zoonotic origin or more simply put to have been caused due to increased interaction between humans and wildlife as a result of anthropogenic activities in terms of environmental degradation and poor planetary health (Murdoch and French, 2020). How did the 2019-nCoV arrive in Wuhan, China is still undetermined, but evidence shows 66% of the 41 initially infected patients had direct exposure to Huanan live animal market (Huang et al., 2020). The COVID-19 pandemic has brought to forefront the urgent need to consider zoonotic and agricultural bridging of novel pathogens along with attention to anthropogenic origins such diseases and human appetite for meat (Kock et al., 2020). COVID-19 is a contagious respiratory and vascular disease and is currently an ongoing pandemic which has already infected about 63 million people world-wide and resulted in 1.5 million deaths as of late November 2020 (WHO, 2020). “Pandemic is not a word to use lightly or carelessly. It is a word that, if misused, can cause unreasonable fear, or unjustified acceptance that the fight is over, leading to unnecessary suffering and death,” said the Director-General of the World Health Organization on March 11, 2020, when the COVID-19 outbreak was declared a pandemic (WHO, 2020b). His key statements to the world included: “prepare and be ready; detect, protect and treat; reduce transmission; and innovate and learn.” The unprecedented repercussions of COVID-19 pandemic have shown the implications of infectious diseases on not only human health and well-being but also on economies and employment even in remote locations (Biswas, 2020; Watts, 2020). World Economic Outlook by the International Monetary Fund projects a deep recession in 2020 and global growth is projected to be −4.4% (IMF, 2020). The International Labor Organization estimates that global labor income has declined by 10.7%, or USD 3.5 trillion, in the first three-quarters of 2020, compared with the same period in 2019 (ILO, 2020).

Environmental Resilience and Transformation in times of COVID-19. DOI: 10.1016/C2020-0-02703-9

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Copyright © 2021 Elsevier Inc. All rights reserved.

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1.  COVID-19: a wake-up call to protect planetary health

Studies have shown that the extent to which the COVID-19 virus induces respiratory stress in infected individuals may also be influenced by the extent to which an individual’s respiratory system is already compromised, including due to air pollution. The result of one study attributes air pollution to be an important cofactor leading to increasing mortality risk from COVID-19 (Pozzer et al., 2020). Another study, in China, shows that high levels of particulate matter pollution may increase the susceptibility of the population to respiratory complications of the disease (Chen et al., 2020). A case study from the United States indicates that COVID-19 associated death rates are raised by about 15% in areas where even a small increase in fine-particle pollution level is observed than in the pre-COVID-19 period (Wu et al., 2020). Adsorption of the COVID-19 virus on airborne dust and particulate matter from air pollution could also contribute to long-range transport of the virus (Comunian et al., 2020). Infected stools in wastewater can generate further transmission routes through the generation of virus-laden aerosols during wastewater flushing. Studies have shown that SARS-CoV can survive in stool samples for 4 days (Lai et al., 2005). A study found that in 2003 contaminated faulty sewage system in a high-rise housing estate in Hong Kong was linked to the SARS outbreak of a large number of residents living in the surrounding buildings (Hung, 2003). Another study also found the presence of SARS-CoV-2 in fecal matter and wastewater and raised the possibility of fecal–oral transmission of SARS-CoV-2 (Heller et al., 2020). Yet another study examined linkages with climatic variables and found a negative association between temperature and COVID-19 infections and a positive association between precipitation and COVID-19 infections (Sobral et al., 2020). Increasingly, there is an interest in understanding the role of climate change, habitat destruction, and urban pollution and their play in the appearance and spread of COVID-19. The objective of this chapter is to examine the trends set in motion by COVID-19 and its implications for planetary health. These will be examined in the context of the long-term environmental and humanitarian repercussions of the pandemic.

1.2  Lockdown as a temporary respite for the environment A growing body of literature examines the impact of COVID-19 on the environment (Prata et al., 2020; Patrício Silva et al., 2021; Yunus et al., 2020; Jribi et al., 2020; Dutheil et al., 2020). As countries locked down and social distancing was enforced in March 2020, many environmental parameters showed an improvement as pollution levels decreased, energy use dramatically reduced, and greenhouse gas (GHG) emissions fell (Watts, 2020). China, the largest global emitter, witnessed a drop in carbon emissions by 25% due to the lockdown; pollution in New York reduced by close to 50% because of measures to contain the spread of the virus; a nationwide lockdown in India a country with the highest pollution levels in the world resulted in a drop of PM2.5 (fine particulate pollutant) by 30% in some cities in just a few days (Mahato et  al., 2020). Studies have also documented positive environmental effects such as reduction of air pollution (especially in terms of PM2.5 and NO2) (Mahato et al., 2020; Sharma, 2020), reduction in noise (Zambrano-Monserrate et al., 2020), and clean beaches (Zambrano-Monserrate et  al., 2020). On the other hand, the negative impacts of COVID-19 have been the increase in volume of infectious waste during the pandemic outbreak due to the lack of waste management of personal protective equipment (PPE), including masks and gloves (Sangkham, 2020). Emissions of GHG have decreased as a result of the pandemic. But this reduction has also only been short-lived with little impact on the total concentrations of GHGs that have accumulated in the atmosphere for decades (Zambrano-Monserrate et al., 2020). The recorded decrease in pollution due to national lockdowns seemed like good news for the environment but it does not by any means imply that climate change is slowing down. Tentative estimates, which project that COVID-19 could trigger the largest ever annual fall in carbon emissions, point to the fact that this fall would not come close to bringing the 1.5°C global temperature limit within reach (CB Analysis, 2020). Global carbon emissions would need to fall by more than 6% every year this decade, which is the equivalent of more than 2200 MtCO2 (metric tons of carbon dioxide) annually, to limit temperature increase to less than 1.5°C above preindustrial levels. Concentrations of carbon dioxide, the gas that is primarily responsible for trapping heat in the Earth’s atmosphere, are up from 413 parts per million this time last year to 416 parts per million now (NOAA, 2020a). As Bill Gates rightly noted in early August 2020 “What’s remarkable is not how much emissions will go down because of the pandemic, but how little. In addition, these reductions are being achieved at, literally, the greatest possible cost” (Gates, 2020). A staggering cost is the impact of the pandemic on the surge of plastic pollution, which threatens to choke the planet.

1. Environmental modifications, degradation and human health risks



1.3  Pandemic reclaiming the plastic usage: demand, production, and usage

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1.3  Pandemic reclaiming the plastic usage: demand, production, and usage Studies estimate that, due to COVID-19, the demand on plastics is expected to increase by 40% for packaging and 17% for medical use and other applications (Prata et al., 2020). Two years ago, the United Nations declared plastic pollution as a global crisis and the year 2020 was meant to mark an ultimate shift away from plastic as countries and cities introduced new bans while scientists and activists argued for positive environmental changes. The coronavirus outbreak and increasing numbers of infections, however, have sidetracked this narrative exerting tremendous pressure on healthcare systems and revealing that plastic is still the most reliable and affordable solution for personal protection (Konov, 2020). Even before the Coronavirus outbreak was declared a pandemic, the global demand for PPE kits saw an upsurge, causing a concurrent uptake in demand for single-use plastics. This escalation in plastic usage has resulted in a global PPE shortage, with a UN task force being constituted to coordinate and scale-up its procurement and distribution. The change in the production trend of PPE between December 2019 and July 2020 was significant as WHO called for industries and governments to increase manufacturing by 40% to meet the rising global demand in March (WHO, 2020). To estimate the number of mask for general public requirements for example the Center for Health Security using a calculation forecasted demand, “10% of the US population of 330 million are essentially house-bound and will not need masks. Of the remaining 300 million, the assumption is 75% will adhere to guidance and wear 1 mask per 5 days on average.” Almost 45,000,000 masks/day, 1,372,500,000 per month until the vaccine arrives and this is for the US population only (Box 1.1). The magnitude of the global waste crisis can be understood from the radical growth in the manufacturing and production of PPE kits which the pandemic has necessitated in the past many months. The global PPE market that was valued at USD 52.7 billion in 2019 is expected to reach USD 92.5 billion by 2025 (VR, 2020) with some of the key global players who include 3M Company, Ansell Limited, Honeywell International Inc., and others. Some of the key players operating in the global PPE market are the M Company, Ansell Limited, Honeywell International Inc., Sioen Industries NV, Kimberly-Clark Corporation, E I DuPont de Nemours and Co., MSA Safety Inc., Lakeland Industries, Alpha Pro Tech, Ltd., Radians Inc., Delta Plus Group, Uvex Safety, Avon Rubber, and Metric AG (Box 1.2). Countries such as India with less waste management capacities have also been producing PPE kits (UMHI, 2020). For example, developing countries like India struggled in arranging PPE kits in the month of March when the lockdown was announced when India had not begun manufacturing PPE kit, and was dependent on other countries like Singapore, Korea, and China for the PPE kits. For India alone, the requirement changed from approximately 4 lakhs to 10 lakhs and more, and has now achieved an almost unrealistic goal of producing 2.06 lakh PPE kits daily within 2 months after the coronavirus outbreak (Box 1.3). The latest demand estimated by Empowered Group-3 of the Government of India is a total requirement of 2.01 crore till the month of June. When WHO provided an update regarding the modes of transmission of the virus, including the possibility of aerosol transmission, face masks were recommended as part of the public health response (WHO, 2020). This led to an enormous increase in the demand for face masks from the public and consequently, their rampant disposal. However, with governments recommending that general populations wear nonmedical, homemade cloth masks to ensure availability of PPE for healthcare workers, it has been forecasted that after adjusting for home-bound individuals, surgical masks will be used by 50% of the population and that they will use, on average, 1 mask per day (Toner, 2020). These figures point to the large magnitude of PPE demand and disposal. According to estimates by WHO, frontline workers, on a monthly basis would need 89 million medical masks, 76 million examination gloves, and 1.6 million goggles (WHO, 2020b; Fig. 1.1). Furthermore, reliance on plastic and imperishable materials has only steadily increased with products such as disposable wipes, cleaning agents, hand sanitizer, disposable gloves, and masks being sold and thrown away in unprecedented volumes (Patrício Silva et al., 2021). This widespread utilization of PPE kits and other self-care products has accelerated during the pandemic period as has the consequent waste generation and resulting GHG emissions involved. This reveals our disregard and inattention to environmental implications when dealing with emergencies. Furthermore, medicine and testing kits have also faced a similar upsurge. On the scale of daily tests conducted in the range of 1000 to 10,00,000, it is found that 50% of the countries are conducting more than 10,000 tests per day and countries like the United States, India, United Kingdom, Russia are conducting 0.5 million tests per day (Fig. 1.2). Lack of stringent regulations and failure to systematically dispose testing kits and other equipment will not only increase the risk of infection, but also increase plastic pollution as all of them ultimately find their way into oceans and landfills. These long-lasting ecological hazards call for governments to focus on sustainable pathways while thinking parallel about the COVID-19 crisis (CSSE-JHU, 2020).

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1.  COVID-19: a wake-up call to protect planetary health

FIG. 1.1  Monthly PPE supplies needed by health workers for COVID-19 response worldwide, 2020.

1100000

779177

444170

206537

200070

35551 India

25043 United States

Russia Tests

196938

16237

14221 France

United Kingdom

Cases

FIG. 1.2  COVID-19: daily tests vs. daily new confirmed cases.

1.4  Waste management: the intensifying crisis Generally, protective materials used by the population such as gloves, masks, or expired medicines are disposed together with domestic waste and garbage. The United Nations Environment Program’s recommendation is that the population should separate these materials and that the local authorities assign municipal operators for the collection or specialized waste management. This recommendation also points out that the safe handling of biomedical and healthcare waste is essential for the health of the community and the integrity of the environment. In the worst-case scenario, it is pointed out that the incorrect handling of said residues could cause a rebound effect on both human health and the environment. The rebound effect for health in the context of COVID-19 are reinfections 1. Environmental modifications, degradation and human health risks



1.5  Ocean pollution and landfills

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due to poor sanitation and exposure to biomedical waste if the additional waste due to COVID-19 is not treated and handled properly. However, rebound effect with reference to environment means the worsening of environmental parameters due to waste management as well as energy related to waste management if additional self-care and medical waste due to COVID-19 is not treated and handled properly. On the International Day for Biological Diversity, the UN Secretary-General, Antonio Guterres stated: “COVID19, arising from nature, has highlighted the intimate connection that exists between human health and our relationship with the natural world” (Guterres, 2020b). He stated that to mitigate climatic disturbances, guarantee food and water security as well as to prevent future pandemics, it is essential to conserve and sustainably manage biological diversity. “Our solutions lie in nature,” he emphasized and recalled that the invasion and human plunder of nature affects the future of humanity (Guterres, 2020a). For example, in countries like India, where about 78% of biomedical waste used to be treated in 198 Common Bio-Medical Waste Treatment Facilities and 225 captive incinerators, the overwhelming rise in biomedical waste caused by COVID-19 has set an unattainable demand for Common Bio-Medical Waste Treatment Facilities (Singh, 2020). Approximately 30% of the medical waste such as masks, gloves, and PPEs have been found dumped outside the hospitals or even the roads (Sharma, 2020). If mishandled, the large volume of plastic and hazardous waste generated during this period will jeopardize the environment and human health. Proper management of biomedical waste generated by hospitals and by self-care, such as medical packaging and contaminated masks, gloves, and used or expired medicines, is imperative. In addition to treating hazardous waste from treatment, diagnostic and quarantine facilities, the greater challenge arises from household waste during the pandemic (Pachauri et al., 2019). The demand for home delivery services of food and groceries has also led to an increase in the generation of common packaging plastic waste containing polypropylene, low-density polyethylene, high-density polyethylene, polyethylene terephthalate (commonly known as PET), polystyrene, and so on. Usage of single-use plastic has bounced back due to growing concerns about hygiene, particularly from products used for personal protection and healthcare purposes (Sharma et al., 2020a). Segregation and collection has been an issue till date and now there is an even more urgent need for segregation of hazardous household waste with the proper disposal of protective suits, gloves, masks, and other waste from hospitals, medical facilities, or clinics through appropriate treatment facilities. Management and handling of plastic waste has become a huge challenge for the waste management industry due to reduced recycling activities in this period. Managing the increase in single-use plastic waste will be a struggle for governments, more so in many developing nations, where mismanaged waste aggregates in town centers or leaks into rivers and oceans, thus triggering new public health crises. Recent experiences from SARS-CoV-2, Ebola, and MERS-CoV disease outbreaks highlight the need for safe biomedical and healthcare waste management for infection prevention and control (Weber et  al., 2016; Rahman et  al., 2020; Ilyas et  al., 2020). If safety issues are not adequately addressed, human health can be put at high risk. Apart from the risk of contact transmission, improper disposal practices of biomedical waste can have adverse environmental effects including soil and groundwater contamination, killing beneficial microbes in septic systems, and physical injuries through sharps among others (Sharma et al., 2020b).

1.5  Ocean pollution and landfills The generation and mismanagement of waste discussed in the sections above have direct implications on water bodies and the state of the ocean. It is known that globally all polluting materials find the ocean as their final destination. In the context of COVID-19, research conducted by the World Economic Forum 6 weeks into the initial outbreak in China, showed that waterlogged masks, gloves, hand sanitizer bottles, and other biomedical and self-care waste was already found on beaches and sea beds in Hong Kong, joining the day-to-day detritus in the marine ecosystems (Stokes, 2020). These materials are transported downstream from upstream by the current of the rivers. Pollutants invariably travel toward the coast no matter how far their original source is located. Furthermore, the dispersion of pollutants also happens by dragging through wind, rain or by direct input via agricultural drains and aquaculture channels (Mallin et al., 2001; Páez-Osuna et al., 1998; Vikas and Dwarakish, 2015; Lu and Turco, 1994, 1995). It is considered that approximately 80% of marine pollution has its origin in terrestrial sources (NOAA, 2020), that is to say that any activity carried out on land will have a significant effect on the ocean–coastal region, threatening not only the health of the great ecosystem but also its productivity and the biodiversity of the marine environment. In other words, the effect of human activities is one of the main causes of marine pollution and its impact on the marine and coastal ecosystems on which the global economy and the health of the ocean depend (Denchak, 2020). 1. Environmental modifications, degradation and human health risks

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1.  COVID-19: a wake-up call to protect planetary health

The areas most impacted by pollution are mainly the productive marine ecosystems such as estuaries, mangroves, and coastal waters. That is why the Global Environment Facility Program, which links freshwater to the ocean, currently known as Global Program of Action, was established under the auspices of United Nations Environment Program. The aim of this program is to establish the link between basins and coastal management, particularly in areas affected by the quality, use, and availability of freshwater. This is a link that must be committed to and acknowledged by various actors and governments from local, national, regional, and global levels. An increase in the presence of all types of unsegregated waste is also observed in both rural and urban areas, either in open dumps or in the field, in inland water bodies, or in municipal waste reception plants (NOAA, 2020; Denchak, 2020; Wong, 1998; Otterpohl et  al., 1997; Patwa et  al., 2020; Han et  al., 2018, 2019a, 2019b; Van Beukering et  al., 1999). This is commonly observed, particularly in countries that do not have adequate waste management programs. The global community must place long-term implications of unchecked ocean pollution on planetary health (WOO, 2020).

1.6  Exacerbated inequalities and vulnerabilities The consequences of human-induced climate change and environmental degradation will progressively have undiscriminating impacts on all human populations, sparing no region or community in the coming decades (Gillis, 2020). However, none can ignore the evidence that demonstrates the disproportionate impact on the socially disadvantaged populations who face the risk of increased exposure and susceptibility to the adverse impacts and the damages caused, and decreased ability to cope with and recover from the losses suffered (Islam and Winkel, 2020). While the field of planetary health fundamentally argues that ecological systems have a bearing on human health, this has become evident not only through the anthropogenic disruption of the natural balance and the consequent pandemic outbreak, but also through the vulnerabilities of poor populations whose lack of power or control over their environment has placed them at the highest risk during this pandemic (Whitmee et al., 2015; Ahmed et al., 2020). The COVID-19 pandemic not only reminds the world of declining planetary health, but as an extension, brings these exacerbated inequalities into the spotlight and ousts the myth that “everyone is in the same boat” (Guterres, 2020b; Mijs, 2020). To begin with, unequal access to safe housing and inadequate access to affordable food aggravates the risk of respiratory conditions, heart diseases, and diabetes, due to a lack of nutrition, and increased exposure to indoor and outdoor air pollution and damp conditions. Scholars have also pointed to climatic factors leading to increased vulnerability to COVID-19 as people who are vulnerable to extreme heat are also vulnerable to COVID-19, this includes the elderly, outdoor workers, and the homeless (Golechha and Panigrahy, 2020). In addition to high-density living conditions that make physical distancing impossible, employment insecurity forces individuals to step outdoors risking contact with the pathogen. This predicament is further compounded by the inability to perform basic, yet essential protective measures like handwashing, which requires assured access to water and sanitation. Unequal access to affordable healthcare is another barrier. This concentration of vulnerabilities to declining planetary health and man-made environmental disasters demonstrates the hamartia of modern society, which is the flawed and disruptive growth structure and development paradigm (Oni, 2020). In April 2020, a study on the COVID-19 impacts on global poverty predicted that over 140 million people could fall into extreme poverty in 2020, with 80 million in Africa and 42 million in South Asia, if governments fail to provide adequate fiscal stimulus and social safety nets (Laborde et al., 2020). The exponential spread of the pandemic since April 2020 forecasts that this estimate could be much greater. Perpetuated by this socioeconomic deprivation is the looming food security crisis much greater in intensity than ever witnessed in the last 50 years (UN, 2020a). Calling COVID-19 as “the Hunger Virus,” Oxfam published a report in early July 2020 stating that 121 million more people could be pushed to the brink of starvation in 2020, with 12,000 people potentially dying per day due to COVID-related hunger and the emergence of new hunger hotspots across the globe. As Oxfam’s Interim Executive Director Chema Vera said, “COVID-19 is the last straw for millions of people already struggling with the impacts of conflict, climate change, inequality and a broken food system that has impoverished millions of food producers and workers. Meanwhile, those at the top are continuing to make a profit: eight of the biggest food and drink companies paid out over $18 billion to shareholders since January even as the pandemic was spreading across the globe—ten times more than the UN says is needed to stop people going hungry” (Oxfam, 2020a, 2020b) Box 1.4. Information on dividend payments of eight of the world’s biggest food and beverage companies up to the beginning of July 2020 have been collected by Oxfam with numbers being rounded to the nearest million: Coca-Cola ($3522 m), Danone ($1348 m), General Mills ($594 m), Kellogg ($391 m), Mondelez ($408 m), Nestlé ($8248 m for entire year), PepsiCo ($2749 m), and Unilever (estimated $1180 m).

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1.7 Recommendations

9

The pandemic has unveiled profound humanitarian concerns never witnessed before and it is complex to determine precisely how the pre-existing social inequalities have intensified. Nevertheless, as new vulnerabilities continue to emerge, the global community will find planetary health and environmental justice too critical to ignore.

1.7 Recommendations “The patient Earth is sick. Global environmental disruptions can have serious consequences for human health. It’s time for doctors to give a world diagnosis and advice on treatment,” wrote Per Fugelli, a Norwegian physician in 1993, way before the field of planetary health was recognized by the global community (Casassus, 2017). This “treatment” necessitates radical redefinition of the models of development adopted by governments, which, till now, have promoted production and consumption at any cost (Casassus and Per Fugelli, 2020). Four of the planetary boundaries have now been crossed as a result of human activity; these include climate change, loss of biosphere integrity, land-system change, and altered biogeochemical cycles (phosphorus and nitrogen; SRC, 2015). Climate change and biosphere integrity are core planetary boundaries and significantly altering either of these two core boundaries, could drive the Earth System into a new state of destruction (Rockström et al., 2009; Steffen et al., 2015). Concerns for planetary health especially due to climate change and biosphere integrity especially remain. The Intergovernmental Panel on Climate Change has time and again sought to warn on the dire impacts of climate change in terms of extreme events, ocean acidification, and socioeconomic implications (IPCC, 2014, 2018). The growing body of literature which examines the causes and consequences of COVID-19 on the environment also explores solutions to address the challenges that confront human and planetary health. Recommendations are presented below with implications for governments, policymakers, and advocates to be creative in designing strategies for preparedness in embracing of health, socioeconomic vulnerabilities, sustainable production, and waste management. Further, evolving multistakeholder and cross-sectoral conversations remain key to understanding the links between fragile biodiversity and increased human risk to zoonotic diseases. As COVID-19 serves as a wake-up call to urgently tackle mounting planetary and human health crises, recommendations are presented below for global governments, policymakers, researchers, program managers, business, and academia among others to urgently address these issues. 1. Factoring climate change in planetary health is essential which would need engaging with adaptation and socioeconomic implications along with multiscale temporal and spatial approaches. For future preparedness to pandemics with zoonotic origins, there is an urgent need to monitor and report indicators at various scales at the regional, national and subnational levels. Two focal metrics of species diversity and human risk of exposure to zoonotic diseases must be monitored and taken into account (Ostfeld, 2017). 2. A systems-level approach from companies and governments on a global scale is required to be adopted in policymaking to address the issue of planetary health and environmental protection. Along with planetary scale issues through climate action and addressing biodiversity loss, safe handling, and sustainable management of the plastic and biomedical waste are vital elements of defining new models for development. 3. Balancing the COVID-19 response with wider health needs to prevent the already stretched healthcare system from being overwhelmed, government bodies should focus on leveraging existing health intervention capacities for outbreak response. For example, WHO delivered PCR testing supplies to test for COVID-19 (these testing kits including new cartridges for the GeneXpert equipment are widely used in the world for testing tuberculosis as well). 4. More relevant research needs to be conducted so that it provides enough evidence. This evidence can then be used to create the awareness needed to develop and promote new planetary health technologies. 5. COVID-19 and the growing awareness of zoonotic diseases should be a part of mainstream narratives and conversations with more engagement of policymakers and pressure groups such as opposition parties, nongovernmental organizations, media and even youth. Multistakeholder conversations need to be informed by science on planetary health and the growing risk of zoonotic infections. 6. Each local or municipal authority needs to develop risk-based contingency plans to manage future outbreaks of zoonotic diseases along with ensuring that essential waste management services. These services should be uninterrupted and add no extra health risks on top of pandemics. There is a need for informed long-term planning so that future health, environmental, and socioeconomic risks are addressed well in advance. 7. Businesses should invest more in sustainable innovation, using environmentally friendly products and renewable resources. For example, glove manufacturers are shifting their focus toward greener manufacturing

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1.  COVID-19: a wake-up call to protect planetary health

technologies by employing renewable resources such as solar and wind power (GVR, 2020). Through use of renewable technologies businesses must strive to reduce costs by using less water and synthetic fuels. This approach, if widely adopted, is expected to spur further product demand over forecasted periods, while also protecting the environment. 8. Linked to the previous point, using bio-based materials to produce PPEs, will offset some of the negative environmental impact due to the current usage of PPE. New PPE producers are already collaborating with biological experts to develop PPE made from bio-based materials in an effort to promote sustainability. For example, Japan-based Bioworks Co. Ltd. has designed a washable and reusable antibacterial face mask made of biomass-based yarn also termed as poly lactic acid (AMR., 2020).

1.8  COVID-19 calls for reflection—conclusion Planetary health approaches are needed to deal with the multiscale issues related to environmental quality and human well-being. Today, three-quarters of the land-based environment and about 66% of the marine resources have been significantly altered by human exploitation (IPBES, 2019a). More than a third of the world’s land surface and nearly 75% of freshwater resources are now used for plant or livestock production (IPBES, 2019a). The expanding human footprint resulting in habitat loss and fragmentation disrupt critical animal behaviors and risk extinction of one million species of flora and fauna, many of which are predicted to be forced into extinction within just decades (IPBES, 2019). Biodiversity loss is associated with the emergence of zoonotic infectious diseases (Civitello et  al., 2015). Ecosystems in disturbed or depleted state can affect emergence of zoonotic pathogens in part due to a reduced “dilution effect” on principal disease reservoir species (Ostfeld, 2017; Ostfeld and Keesing, 2000; LoGiudice et al., 2003; Khalil et  al., 2016). Dilution effect indicates that the species vary in susceptibility, which are infected by a pathogen and the higher diversity often leads to lower infection in hosts as nonhost species dilutes the infection. Advancing land-use frontiers to biodiversity rich areas not only have negative implications for planetary health but also increase frequency of ecotones (areas where there is increased interactions between humans and wildlife) which result in the onset of zoonotic infectious diseases (Rohr et al., 2019). Climate change would only exacerbate the outbreaks of infectious diseases due to human and wildlife contact due to shifting wildlife migration along with habitat destruction due to wildfires and climate extremes (Jenkins et al., 2020). The foreseen danger that lies ahead, is the lack of measures adopted by governments in addressing this unsurmountable issue as they are forced to divert their attention and employ existing time and resources to overcome the pandemic without regarding the larger picture of long-term environmental and humanitarian consequences. The rise in magnitude of the production of self-care products will be further compounded with the production and dissemination of vaccines as governments race to control and end the acute phase of the pandemic by 2021. Without a strategic mechanism to address the anticipated biomedical waste accumulation, there will be growing pressure on the existing quality of treatment and disposal. The policies for the management of medical waste, in general and, in particular, for those from the management of patients with COVID-19, have been suggested by the UN considering factors such as: generation and minimization, separation, identification and classification, handling and storage, packaging and labeling, internal and external transportation, treatment, waste disposal, including emissions, occupational health and safety, public and environmental health, awareness, and education and research. These factors are contained in the Technical Guidelines on the environmental management of biomedical and sanitary waste of the United Nations Basel Convention on the Control of Transboundary Movements of Hazardous Wastes (UN, 2020b). In light of the building global waste crisis and the vast realm of impending environmental consequences, human rights concerns have become another undeniable reality that the world has to confront. The impacts of COVID-19 have revealed that it is those at the margins, who are already in a state of vulnerability are pushed to new extremes. According to the study carried out by United Nations Economic Commission for Latin America and the Caribbean and UNDP, the main consequence of COVID-19 pandemic will be a “profound social inequality, distribution of long-term resources and equal opportunities in different dimensions,” the latter two presenting themselves as great challenges to face. Adopting a strong humanitarian and planetary consciousness and proactively embracing the five pillars of sustainable development is the fundamental need of the hour (Neidhöfer, 2020). The COVID-19 pandemic is a wake-up call to the fact that, if governments continue business as usual, devastation of the Earth’s landscape for “development” will persist at the cost of the planet’s natural resources and threaten the very survival of all species. Such a model of development, which serves governments’ insatiable appetite for “development” putting profit over the cost of life, must be radically redefined. Active and visionary leadership from 1. Environmental modifications, degradation and human health risks



1.8  COVID-19 calls for reflection—conclusion

11

world governments is urgently needed to redefine approaches to development, which will be a stark departure from the environmental desecration the world has witnessed in recent decades. As countries scale up responses to COVID19, an opportunity exists to align with the proposed redefined values of development, which embrace a safer planet and a promise of improved health for all. Positive, sustainable environmental impacts demand long-term changes in production and consumption norms. These changes will be necessary in both rich and poor countries and will demand making radical shifts in political focus. The role of public opinion in compelling such a change will be key. As people become more aware of their dependence on the environment, governments must focus on effective science–policy interface or changes in policy, which is informed by science. A strengthening of international scientific partnerships and collective action is needed for all governments to deal with the challenge of redefining models of development to improve the lives of all species and protect our planet (Colglazier, 2020; POP Movement, 2020). The current disruptions due to COVID-19 are likely pale in comparison to the upheavals in store, if governments do not act aggressively to limit warming to less than 1.5°C above preindustrial levels and adopt cleaner and healthier models of development. As governments think about the world post-COVID-19 and prepare for what comes next, they must closely examine how their actions to protect our planet can be part of the new world. It is clear, as the world grapples with many unknowns, the one thing that is known is that the health of the planet and those that inhabit it are inextricably linked.

BOX 1.1

Forecasting demand of mask To estimate the number of mask for general public requirements for example the Center for Health Security using a calculation forecasted demand, “10% of the US population of 330 million are essentially housebound and will not need masks. Of the remaining

300 million, the assumption is 75% will adhere to guidance and wear 1 mask per 5 days on average.” Almost 45,000,000 masks/day, 1,372,500,000 per month until the vaccine arrives and this is for the US population only.

BOX 1.2

Some of the key players in the global PPE market Some of the key players operating in the global personal protective equipment market are the 3M Company, Ansell Limited, Honeywell International Inc., etc. [Box 2] Sioen Industries NV, Kimberly-Clark

Corporation, E I DuPont de Nemours and Co., MSA Safety Inc., Lakeland Industries, Alpha Pro Tech, Ltd., Radians Inc., Delta Plus Group, Uvex Safety, Avon Rubber, and Metric AG.

BOX 1.3

Change in demand and supply trend of PPE; India For example, developing countries like India struggled in arranging protective equipment (PPE) kits in the month of March when the lockdown was announced when India had not begun manufacturing PPE kit, and was dependent on other countries like Singapore, Korea, and China for the PPE kits. For India alone, the requirement changed

from approximately 4 lakhs to 10 lakhs and more, and has now achieved an almost unrealistic goal of producing 2.06 lakh PPE kits daily within 2 months after the coronavirus outbreak. The latest demand estimated by Empowered Group-3 of the Government of India is a total requirement of 2.01 crore till the month of June.

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1.  COVID-19: a wake-up call to protect planetary health

BOX 1.4

Dividend payments information of biggest food and beverage companies; July 2020 Information on dividend payments of eight of the world’s biggest food and beverage companies up to the beginning of July 2020 have been collected by Oxfam with numbers being rounded to the nearest million: Coca-Cola

($3522 m), Danone ($1348 m), General Mills ($594 m), Kellogg ($391 m), Mondelez ($408 m), Nestlé ($8248 m for entire year), PepsiCo ($2749 m), and Unilever (estimated $1180 m).

References Ahmed, F., Ahmed, N., Pissarides, C., Stiglitz, J., 2020. Why inequality could spread COVID-19. Lancet Public Health 5 (5), e240. AMR, 2020. Healthcare Personal Protective Equipment Market Outlook—2027. Allied Market Research (AMR), Portland. Biswas, S., 2020. India Coronavirus: Covid Strikes Remote Greater Andamanese tribe. BBC News. https://www.bbc.com/news/world -asia-india-53925071 (Accessed 12 March 2021). Casassus, B., 2017. Per Fugelli. Lancet 390 (10107), 2032. CB, 2020. Analysis: coronavirus set to cause largest ever annual fall in CO2 emissions. Carbon https://www.carbonbrief.org/analysis-coronavirus-set-to-cause-largest-ever-annual-fall-in-co2-emissions. (Accessed 12 March 2021). Chen, K., Wang, M., Huang, C., Kinney, P.L., Anastas, P.T., 2020. Air pollution reduction and mortality benefit during the COVID-19 outbreak in China. Lancet Planet. Health 4 (6), e210–e212. Civitello, D.J., Cohen, J., Fatima, H., Halstead, N.T., Liriano, J., McMahon, T.A., Ortega, C.N., Sauer, E.L., Sehgal, T., Young, S., Rohr, J.R., 2015. Biodiversity inhibits parasites: broad evidence for the dilution effect. Proc. Natl. Acad. Scie. 112 (28), 8667. Colglazier, W., 2020. Response to the COVID-19 Pandemic: Catastrophic Failures of the Science-Policy Interface. Science & Diplomacy. American Association for the Advancement of Science (AAAS). https://www.sciencediplomacy.org/editorial/2020/response-covid19-pandemic-catastrophic-failures-science-policy-interface. (Accessed 12 March 2021). Comunian, S., Dongo, D., Milani, C., Palestini, P., 2020. Air pollution and Covid-19: the role of particulate matter in the spread and increase of Covid-19′s morbidity and mortality. Int. J. Environ. Res. Public Health 17 (12), 4487. CSSE-JHU, 2020. COVID-19: Daily Tests Vs. Daily New Confirmed Cases. Center for Systems Science and Engineering, John Hopkins University (CSSE-JHU), Baltimore, Maryland. https://ourworldindata.org/grapher/covid-19-daily-tests-vs-daily-new-confirmedcases?tab=chart&stack Mode=absolute&time=latest&country=®ion=World. (Accessed 12 March 2021). Denchak, M., 2020. Ocean Pollution: The Dirty Facts. Natural Resources Defense Council. https://www.nrdc.org/stories/ocean-pollution-dirty-facts. (Accessed 12 March 2021). Dutheil, F., Baker, J.S., Navel, V., 2020. COVID-19 as a factor influencing air pollution? Environ. Pollut. 263 (Pt A), 114466. Gates, Bill, 2020. COVID-19 Is Awful. Climate Change Could be Worse. Gates Notes. https://www.gatesnotes.com/Energy/Climate-andCOVID-19. (Accessed 12 March 2021). Gillis, J., 2020. Panel’s Warning on Climate Risk: Worst Is Yet to Come. The New York Times, New York. https://www.nytimes. com/2014/04/01/science/earth/climate.html. (Accessed 12 March 2021). Golechha, M., Panigrahy, R.K., 2020. COVID-19 and heatwaves: a double whammy for Indian cities. Lancet Planet. Health 4 (8), e315–e316. Guterres, A., 2020a. ‘Inequality Defines Our Time’: UN Chief Delivers Hard-Hitting Mandela Day Message. United Nations, New York. https://news.un.org/en/story/2020/07/1068611. (Accessed 12 March 2021). Guterres, A., 2020b. Secretary-General’s remarks to United Nations Biodiversity Summit [as Delivered]. United Nations, New York. https:// www.youtube.com/watch?v=5WoQ0JjMzP8&t=485s. (Accessed 12 March 2021). GVR., 2020. Wind Power Market Size, Share & Trends Analysis Report by Location (Onshore, Offshore), by Application (Utility, Non-Utility), by Region (North America, Europe, APAC, South America, MEA), and Segment Forecasts, 2020–2027. (GVR) GVR Ed. Grand View Research (GVR), San Francisco. Han, Z., Liu, Y., Zhong, M., Shi, G., Li, Q., Zeng, D., 2018a. Influencing factors of domestic waste characteristics in rural areas of developing countries. Waste Manage. 72, 45–54. Han, Z., Ye, C., Zhang, Y., Dan, Z., Zou, Z., Liu, D., 2019. Characteristics and management modes of domestic waste in rural areas of developing countries: a case study of China. Environ. Sci. Pollut. Res. 26 (9), 8485–8501. Han, Z., Zeng, D., Li, Q., Cheng, C., Shi, G., Mou, Z., 2019b. Public willingness to pay and participate in domestic waste management in rural areas of China. Resour. Conserv. Recycl. 140, 166–174. Heller, L., Mota, C.R., Greco, D.B., 2020. COVID-19 fecal-oral transmission: are we asking the right questions? Sci. Total Environ. 729, 138919. Horton, R., Beaglehole, R., Bonita, R., Raeburn, J., McKee, M., Wall, S., 2014. From public to planetary health: a manifesto. Lancet 383 (9920), 847. Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395 (10223), 497–506. Hung, L.S., 2003. The SARS epidemic in Hong Kong: what lessons have we learned? J. R. Soc. Med. 96 (8), 374–378. ILO, 2020. COVID-19 Leads to Massive Labor Income Losses Worldwide. International Labor Organization, Geneva. https://news.un.org/en/ story/2021/01/1082852. (Accessed 12 March 2021).

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1. Environmental modifications, degradation and human health risks

C H A P T E R

2 Zoonotic disease in the face of rapidly changing human–nature interactions in the Anthropocene Shamik Chakrabortya, Pankaj Kumarb, Binaya Kumar Mishrac a

Hosei University Tokyo, Japan Natural resources and Ecosystem Services, Institute for Global Environmental Strategies, Hayama, Japan c School of Engineering, Pokhara University, Nepal

b

2.1  Introduction: why zoonotic diseases can be a concern in the Anthropocene The “Anthropocene” is the age dominated by humans and is an unofficial unit of time where humans have become a major geologic, geomorphic and ecological force, causing long-term changes in the earth’s ecosystems (including geologic and geomorphic processes). Humans move earth and soil per year more than the fundamental earths processes like rivers and glaciers (Ball, 2005). The last 10,000 or so years or the Holocene, in which humans evolved, have been a stable period, except the changes brought about by humans who has crossed important planetary boundaries. Some examples include climatic change due to anthropogenic causes, alteration of biogeochemical cycles, and the loss of biological diversity (Rockstrom et al. 2009; Steffen et al., 2015). Perhaps the most widespread and most significantly crossed of these planetary boundaries (and widely talked about consequence) is the global decline in biodiversity. The major causes for loss of biodiversity include expansion of agriculture, which is highly constrained, at the expense of wild and intact ecosystems, increase of urban areas, increased wildfire incidences, and introduction of alien species into native ecosystems (Sala et  al., 2000). Furthermore, these processes are highly interlinked giving their complex nature. For example, increase in agricultural areas is often manifested by deliberate forest fires; as observed in the Amazon basin wildfire incidents, which are linked to socioeconomic characteristics in a society (Lindsey, 2004). Introduction of alien species may also be due to agricultural and animal husbandry practices. Crossing of planetary boundaries in the Anthropocene means limiting the livable conditions or the safe operating spaces, exposing ecosystems, including human managed ecosystems, to more extreme conditions that occur at planetary scale. These include crop failures in different parts of the world under changing climate, lesser diversity of food, and increased outbreak of diseases. All these lead to less resilience of the vital ecosystems such as agricultural and forested areas. As these two types of ecosystems are spread in wide varieties over the world and our survival depends the diversity and spatial extent of them the degradation of these ecosystems can raise questions on human survival in the future. This lesser (and declining) diversity of the ecosystem is quite evident in our lifestyle and diet. In today’s world about 75% of the food is generated from 12 plant and five animal species (FAO, 2004). Production of meat has increased manifold in the last several decades as total meat production has increased from less than 1 million tons in 1960s to about 350 million tons in 2018, with biggest increase taking place in Asia (Ritchie and Roser, 2017). The higher production of meat is an indicator of handling of animals by humans. For example, in 2018, close to 70 billion chickens have been slaughtered in the world, and more than 120 million tons of meat have been produced by China and USA alone (Ritchie and Roser, 2017). The loss of agrobiodiversity is also highly interlinked with loss of forest cover, wetlands, and other aquatic ecosystems, which exacerbate this loss in the diversity of consumable plants and animals (FAO, 2004), making us to depend on more animal-based diets. This state arguably leads to lesser resilience Environmental Resilience and Transformation in times of COVID-19. DOI: 10.1016/C2020-0-02703-9

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in the ecosystems that are managed and/influenced by humans. This resilience is also increased by increased expansion of human manages ecosystems. Scholars have argued that expansion of agriculture and animal husbandry systems has jeopardized the planets ecosystem diversity, and this loss of diversity can also contribute to increase in zoonotic diseases (Ellewanger et  al., 2020; Morand, 2020). Thus, it is high time that we should provide clear messages about this relation between biodiversity loss, agricultural expansion, (including in increase in livestock production and consumption, and zoonotic diseases (Bogueva and Marinova, 2020). These arguments makes the case that our diet and nutrition acquisition pathways needs to be critically analyzed and bettered to reduce future impact of zoonotic diseases in the society. Based on the above premise, we hypothesize in order to stimulate a debate that the loss of biodiversity in the Anthropocene can open pathways for animal hosts that carry bacteria/virus into the increasingly human dominated, commercialized, less diverse agriculture, animal husbandry, and as hunting/gathering activities. We create a narrative through reviewing key recent literature in this issue. The chapter is divided into five sections. Following introduction, Section 2.2 discusses the concept of resilience and its relation to biodiversity loss and zoonotic diseases. Section 2.3 presents the case of some notable zoonotic diseases of viral origin while showing their relation to unsustainable land use practices. Section 2.4 discusses about possible measures to fight next pandemics with concept of resilience before concluding the main arguments of the chapter in conclusion section.

2.2  Resilience and its change due to biodiversity loss and diseases The definition of Resilience by Holling (1973), and Holling and Gunderson (2002) is the amount of disturbance a system can absorb without changing its state. The Resilience Alliance (2002) states resilience as the ability of a system to absorb disturbances or changes, characterized by its ability to retain its basic function to reorganize itself, that is, retain its basic structure and functions in spite of changes. More biological diversity is related to more resilience through flows of ecosystem functions (Cardinale et  al., 2012; Loreau and Mazancourt, 2013). Decreasing biodiversity can reduce certain function of ecosystems such as pollination, carbon sequestration, pest control, and decomposition among others, leading to loss of resilience in the ecosystem (Tilman et al., 2006, Dovčiak and Halpern, 2010; Schnitzer et al., 2011; Oliver et al., 2015). This loss of resilience has been observed in case of disease outbreaks also, that affect plants and animals. For example, if high yielding varieties are chosen in agricultural fields, these varieties also become vulnerable to pests and diseases, conditions which the commercial crops with less genetic diversity cannot withstand (Scialabba et al., 2003). This story is similar for aquaculture as well; as the species chosen and managed through aquaculture are observed to be more disease prone than wild capture fisheries (Leung and Bates, 2012; Murray and Peeler, 2005). Often these diseases are brought in by ecological changes and with human interventions (Jones et al., 2008). Indigenous species are argued to be better equipped to withstand the shocks such as pests, diseases and climate change (Scialabba et al., 2003; Altieri, 2015). Also, many indigenous species that humans use for agriculture or use directly for food are in our diet through centuries and is the result of years of indigenous knowledge on food consumption from plants and animals. The changes seen in the Anthropocene (such as crossing of planetary boundaries as argued by Rockstrom et al. (2009) and Steffen et al. (2015) indicates that the way ecosystems, including animals and humans interact, will continue to change in the coming decades, giving us a degraded and less resilient landscape to adapt with (United Nations Environment Programme and International Livestock Research Institute, 2020). For example, although humans contitute only about 0.01% of the planet’s biomass, human activities have reduced marine and terrestrial mammalian biomass by 6 times (Bar-On et al., 2018). Doughty et al. (2020) have argued that decrease and extinction of megafauna in the late quaternary can be a cause of increase of zoonotic diseases. How this whole situation has an influence on outbreak of diseases of zoonotic origin is under researched, but it is speculated that unsustainable land use systems (such as deforestation and decrease in biodiversity) coupled with climate change can aggravate the condition on how diseases get entry to human society (Ellewanger et  al., 2020; Morand, 2020). For example, unsustainable land uses can lead to habitat fragmentation, which can make the infectious diseases coevolve with changing host and diversifying in the process (Zohdy et al., 2019), increasing the different pathways through which zoonoses can enter human society. In fact, zoonotic diseases and human influence on earths ecosystems are (arguably) so closely connected that to reduce the threat of future zoonotic diseases, researchers have suggested to have an integrated approach, taking the human and animal environment (HAE) in a single approach (Webster et al., 2016; Mwangi et al., 2016; Tastan and Ak Can, 2019). 1. Environmental modifications, degradation and human health risks



2.3  The case of zoonotic diseases

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2.3  The case of zoonotic diseases According to Graham and Corbett (2020), out of 100 viral families, 25 can infect humans. There are 120 viruses within these 25 families that can pose risk to humans. However, there are only 13 legalized vaccines available and this means that there are considerable gaps in knowledge about viral diseases and their cures. Many of these viruses, including COVID -19 are zoonotic in origin and are carried by a vector animal, most often, rodents and bats, which are called reservoir hosts. These zoonotic diseases can also be unleashed through intermediate hosts such as domestic dogs, cats, primates, and farm animals (Graham and Corbett, 2020). Studies about zoonotic diseases report that about 60% of the diseases to humans are of zoonotic origin. Present knowledge on zoonotic diseases is very limited and the awareness is recently stimulated by the COVID-19 pandemic. However, it is evident that our biosphere is not devoid of the diversity of zoonoses. The greatest concentration of species with zoonoses are found on the Amazon basin area, Central and Western Europe, and West Africa, followed by North America, central Russia, Southeast Asia, and Central Africa (Han et al., 2016). Most of the zoonotic diseases are related to domesticated animals such as pigs, chicken, cows, and goats. This shows a trend of zoonotic disease transfer to humans from domesticated animals in human managed ecosystems. These domesticated animals are thought to take in new viruses from wild hosts and these zoonoses can coevolve and diversify as argued by Zohdy et al. (2019). In fact, among zoonotic diseases, only Lassa fever has been caused by the multimammate rat, which is a wildlife host that directly affected humans (United Nations Environment Programme and International Livestock Research Institute, 2020). Therefore these arguments stimulate debate that zoonotic diseases can increase in the Anthropocene due to decrease in biodiversity and increased use of animals for food and nutrition. Fig. 2.1 shows the possible pathways of transfer of zoonotic diseases through a simple schematic diagram. The diagram shows how the landscape of zoonotic diseases may look like under declining biodiversity with increasing encroachment of wild and intact ecosystems (including how they are transferred to humans and can grow to pandemic proportions). Changes in ecosystems due to anthropogenic disturbances are guided by key factors such as changes in species diversity in a given landscape, opportunity of better virus survival rate in the environment, and chances of hostvirus co-evolution. High biodiversity can reduce the chances of transfer of viruses to a single species as the densities of the host species are less in a given area (Han et  al., 2016). This characteristic of biodiversity on the spread of zoonotic disease may in fact the cause of some recent findings that human managed ecosystems house more species that carry infectious diseases as hosts. With more and more land being managed by humans there will be more chances of spillover of the pathogens that originate in animals. This can make human managed systems less resilient to diseases. Zoonoses and their hosts are quite ubiquitous in the natural world (Han et al., 2016), and they exist around us. Below, we discuss 6 notable zoonotic diseases that are related to virus. The list is used as a crude, but useful window to show and how zoonotic diseases are connected with wild animal hosts.

2.3.1 Influenza Among different types of influenza viruses one subtype of type A influenza is zoonotic. In fact, the biggest outbreak of pandemic in recorded history was caused by the 1918 influenza virus (H1N1) (also called “Spanish flu”). It is estimated that roughly, the virus killed 50 million people and arguably affected one-fourth of the world’s population at that time. H1N1 was a zoonotic virus of avian origin adapting in a mammalian host before affecting humans (Jordan et al., 2019). Different strains of the virus have affected human society afterward such as in 1957, 1968, and 2009, but with less severity (Jordan et al., 2019). The influenza virus still continues to take lives of about 300 thousand to 600 thousand people annually (Jabr, 2017). Still very little is known about the virus and how it first spread to humans; a vital piece of information to conclusively track down a virus and speculate its possible pathway of transmission to humans. However, it can be argued that the H1N1 will continue to be an example of how a deadly a virus of zoonotic origin can be and what it can do to a population that has limited or no immunity against it.

2.3.2 HIV HIV or human immunodeficiency virus is a classic case where the host (zoonose) is not pinpointed. It is believed that it may have come from Chimpanzees in West Africa and was transmitted to humans through hunting activities for bush meat consumption. It is argued that HIV entered humans through simian immunodeficiency viruses 1. Environmental modifications, degradation and human health risks

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Wild hosts with zoonoses

Wild and intact ecosystems

Release of zoonotic diseases to intermediate hosts

Greater encroachment of wild areas Direct transmission

Wild and intact ecosystems

Direct transmission

Release of zoonotic diseases to intermediate hosts

' ' Intermediate hosts with zoonoses ' Adaptation of zoonoses for human to human ' ' transmission ' Regional to global pandemics

Human managed ecosystems

Potential road towards pandemic

Legend Encroachment into the wild areas Transmission of zoonotic diseases Potential road towards pandemic Nature of transmission in zoonotic hosts Wild ecosystems Human managed ecosystems Development of pandemic(s) FIG. 2.1  Possible release of zoonotic diseases in the human society due to increasing pressure on wild and intact ecosystems. Source: Authors.

(SIV) around 1900 in what is now the Democratic Republic of Congo (Worobey et al., 2008). In fact, SIV has been found in several primates that are hunted for bush meats including in bush meat hunters (Apetrei et  al., 2005). HIV then started to spread across the world, and it is estimated that about 8-10 million people are living with the virus at present. HIV is a dangerous virus, but its spread is limited to those who come into contact with the body fluids of the patient, thus making it to be able to spread through certain pathways only, which can be avoided with lifestyles and knowledge. In terms of ecosystem resilience, bush meat is a widely discussed topic as hunting activities that reduce present endangered population of primates. Hunting for bush meat has been argued as a major extinction threat and an internal force of irreversible changes in forest ecosystems due to anthropogenic pressure (Ripple et al., 2016). 1. Environmental modifications, degradation and human health risks



2.3  The case of zoonotic diseases

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2.3.3 Ebola Ebola has caused more than 11 thousand deaths in a few years (2013–2016) in Sierra Leone, Guinea, and Liberia mainly, costing the world about 3 billion USD. The first recorded outbreak of Ebola was in 1976 and spread through a community near Ebola River in Zaire. This outbreak was the most fatal with 88% mortality rate (WHO (World Health Organization), 2020). The virus has continued to reemerge and has affected several countries in Africa. Like HIV, Ebola cannot spread except exchange of body fluids from infected persons or infected bats and primates. This makes their spread not so spontaneous. Ebola affects the immunity system and enters human body through cell transport receptors. It then enters live cells and hijacks them. The human body cells themselves house Ebola viruses and releases more viruses from them to other intact cells. The guard cells are infected also and these guard cells send signals to the blood vessels to release body fluids. This starts internal bleeding in the body, which complicates the situation further. The virus also affects vital organs at the same time such as liver. The patient’s body thus undergoes multiple organ failures as a result and in such a case, death is very common. In fact about 50% of Ebola patients with advanced stage may die (higher fatality rate of 90% have also been observed) (WHO (World Health Organization), 2020), this makes Ebola one of the most dreaded diseases in the present world. Ebola is mainly found in the rainforests of Africa and Southeast Asia. In fact majority of Ebola cases can be traced back to handling of carcasses of apes (Pourrut et al., 2005) and shows similar trends of handling bush meat in the rainforest areas of Africa and Southeast Asia, bringing similar arguments of forest exploitation and pressure on remaining forest habitats. It is to be mentioned here that it is most probable that bush meat consumption has been an age-old phenomena in the human society as it is at the core of hunting-gathering activity. However, we are concerned about the hunting pressure for bush meat in increasingly less biodiverse forest areas that are facing forest ecosystem change and habitat fragmentation. It is in these areas where zoonoses can change hosts and diversify including in humans (see Section 2.2).

2.3.4  Avian influenza Similar to COVID-19, avian influenza (H5N1) is also another zoonotic disease that has an economic effect. The recorded animal to human transmission took place with the 1995 outbreak in Hong Kong. Fortunately though avian influenza is not a dreaded disease unlike H1N1, and its entrance into the human society is still rare. But it is feared that the virus can mutate and change itself significantly to which human‘s immune system do not have immunity to; and thereby have a potential to grow to a pandemic proportion. Avian influenza can spread due to production and consumption of chicken in industrial proportion for acquiring animal protein; which the world is presently doing. For example, in 2018 more than 68 billion chicken were slaughtered in the world, followed by pigs (1.48 billion) (Food and Agriculture Organization of the United Nations, n.d.), showing the extremely high level of consumption of meat from avian origin which can become intermediate hosts in case the avian influenza grows to epidemic proportions.

2.3.5 Hantavirus The Hantavirus is a zoonotic disease that causes hemorrhagic fever with renal syndrome (HFRS) and Hantavirus pulmonary syndrome (HPS). Hantavirus is spread mainly by rodents such as rats, and human-to-human transmission is still uncommon. However human-to-human contamination can happen such as in Hantavirus pulmonary syndrome (HPS) that happened in Argentina (Enria and Levis, 2004). When HPS develops in human body, mortality rate becomes high to about 40%. This makes Hantavirus one of the dreaded zoonotic disease. Research shows that Hantavirus can spread more due to high temperatures and increases in agriculture at the expense of native vegetation. These factors can alter abundance of rodent species that act as reservoir host of Hantavirus (Prist et  al., 2017), an argument that shows the nature of Hantavirus to spread through human managed landscapes supported by warming due to climate changes. New Hantavirus have been discovered in zoonoses except rodents (commonly believed as the only host of hantavirus) as well such as crocidurine and soricine shrews, moles and bats (Reusken and Heyman, 2013). This indicates the complex nature of viruses and their interactions through host species. Scientists predict that in the near future there are chances that new Hantavirus strains and their zoonotic hosts will be identified (Reusken and Heyman, 2013).

2.3.6 COVID-19 The COVID-19 is another disease where zoonoses have not been pinpointed. The full form of COVID-19 is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is arguably the most widespread epidemic in human history. It is arguably transmitted from bats, probably in the seafood market in Wuhan, China (The Guardian, 2020).

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2.  Zoonotic disease in the face of rapidly changing human–nature interactions in the Anthropocene

The first recorded case was in late 2019 and the virus spread quickly to pandemic proportion by March 2020 when WHO characterized the disease as a pandemic. The extent of land use change that can be related with the disease is not conclusive, but it is argued that (illegal) wildlife trade - a major force of biodiversity decline- may have an effect with early transmission of the virus from bats to humans or from bats to some other animals such as pangolin before getting its entry into humans. The transmission can be mediated by secondary host species as observed during the SARS (Severe Acute Respiratory Syndrome, which occured in 2002), and MERS (Middle East Respiratory Syndrome, which occured in 2012) cases where the viruses were transmitted through civet cats and camels respectively as secondary hosts (de Wit et al., 2016). Currently more than 118 million COVID-19 cases have been reported from around the world (Worldometers.info, 2021), not mentioning the number of unreported cases, mainly in the developing countries, which have poor health care and support facilities. All these zoonotic diseases have a significant impact on our immune system and new strains of viruses continuously deliver new challenges for the immune system. As we have seen by looking at the nature of (less resilient) land uses in the Anthropocene, that these new strains have more possibility to be spread by more human dominated (human engineered) ecosystems that move closer to wild hosts that carry zoonoses. Although this argument is still at hypothetical stage but it is here where we need to think carefully about possible measures with better land use practices that includes another factor for resilience, which is the case of zoonoses.

2.4  Possible measures to fight next pandemics with concept of resilience Much of the knowledge about zoonotic diseases and their transmission is rudimentary. Researchers have argued that some effective measures against zoonotic diseases may be through preventing exposure to infectious agents from wildlife to domestic interface, and preventing introduction of wild hosts to new areas through human actions (Tompkins et al., 2004). It is here that resilient land use can help. Resilient land use mean land use with greater capacity of “adapt” to changing conditions and maintain its basic function. In the present context, resilient land use is closely connected with planning and management for the use of natural resources that do not hamper the diversity of ecosystems, including functions that are related with diverse ecosystems (Oliver et al., 2015). Example of resilient land use can be conservation of vital (intact) ecosystems including forests, and their functions and greater diversity of crop species in agricultural areas, including nutritional diversity available from the agricultural areas. Similarly, improved management of agricultural production, including livestock production that helps maintain ecosystem diversity (and their functions) intact can be called resilient agricultural and livestock production systems. This can reduce pressure of agriculture on opening up remaining forestlands and getting into contact with new wild hosts unknowingly, giving a zoonotic disease to adapt and enter human society in epidemic form. The effect of population and economic growth can act as indirect drivers to induce land use changes, thus these indirect drivers needs to be dealt carefully as well. Resilient land uses can act as a tool that can work with uncertainty and change; and a classic case is presented by the complex landscape of zoonotic disease and its effects in the human society. It means that these measures may not be enough to prevent all zoonotic diseases to enter human society and to stop the next epidemic with certainty. But it means that we need to think about human-nature interactions in regard to diseases, which can emerge as a new challenge in the Anthropocene for us to deal with.

2.5 Conclusion This chapter raises the argument through a review of recent literature that the reduced resilience in the world’s ecosystems due to increasing human influence can lead to conditions for potential outbreaks of diseases of zoonotic origin. Zoonotic diseases can be a new threat in the Anthropocene, influenced by increasing contacts of humans and livestock to zoonotic hosts, a common pathway that has been the key for outbreaks of zoonotic diseases. It is a mystery how diseases enter human societies and how we adapt our immune system with diseases, and there are many complex factors on how an outbreak maintains itself to have a lasting effect, i.e., rise to pandemic proportions. It is also highly likely, that we may never totally nullify zoonotic diseases entering our society. However, with resilient land and resource use, we may become better equipped to keep ourselves within the boundary of conditions that release zoonotic diseases into our society. This again, is a tough challenge because we have become ever more dependent on a less diverse food system that is based on less diverse land uses eroding remaining intact and wild ecosystems. Less resilient land uses are in fact embedded in the present economic system. But at least it is possible to stimulate debate in the Anthropocene argument with a new problem in it to deal with, which is the case of zoonotic diseases and this chapter raised and revisited some of these arguments. 1. Environmental modifications, degradation and human health risks

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C H A P T E R

3 Impact of Covid-19 lockdown on the socioenvironmental scenario of Indian Sundarban Punarbasu Chaudhuria, Subarna Bhattacharyyab a

Department of Environmental Science, University of Calcutta, Kolkata, India b School of Environmental Studies, Jadavpur University, Jadavpur, India

3.1 Introduction Mangroves are group of salt tolerant coastal species, which inhabit shores of tropical and subtropical regions (Saenger, 2002). The cyclic supply of fresh, nonsaline water is a necessity for their normal growth (Tack and Polk, 1999). They are broadly classified into two groups: (1) true mangroves and (2) mangrove associates (Biswas et al., 2018). True mangroves are species that only thrives in coastal intertidal zones, e.g., Heritiera fomes, Bruguiera gymnorrhiza, Avicennia alba, and Rhizophora mucronata, whereas mangrove associates are those which can survive in both littoral and terrestrial environments, e.g., Hibicus tilisaceus, Suaeda nudiflora, and Thespesia populnea. Mangroves possess several important ecological, societal and socio-economical functions (Bakshi and Chaudhuri, 2014; Pattanaik et al., 2008; Tam and Wong, 1999; Van Lavieren et al., 2012). Despite having important ecological, social and economic functions of mangroves, these coastal ecosystems are continuously threatened due to anthropogenic activities and climatic vulnerability. Globally mangroves cover 156,220 sq. km area (FAO, 2010). The Sundarban is the largest continuous tract of estuarine mangrove habitat situated at South and North 24 Parganas districts of West Bengal (India) and three districts of Bangladesh. The region is bordered by river Baleshwar in the east and river Hooghly in the west. River Harinbhanga separates Indian and Bangladesh part of the Sundarban. Indian Sundarban covers an area of ∼4246 sq. km (Chaudhuri and Choudhury, 1994) in which mangroves covers an area of 2094 sq. km (Forest Survey of India, 2013) and located at a distance of ∼120 km from the India’s third most populous metropolitan city “Kolkata” (Census of India, 2011). Kolkata is located on the east bank of the river Hooghly- it is the principal commercial, cultural and educational city in eastern India. The estuary suffers from gradual environmental degradation due to ever increasing population pressure, economic activities, operation of diesel operated mechanized boats and increasing agricultural and aquaculture practices (Bhattacharya et al., 2015). On December 31, 2019, China informed the World Health Organization (WHO) of a cluster of cases of pneumonia of an unknown cause in Wuhan City, China, the epicenter of the pandemic. Huanan wholesale seafood market is a well-known wet market where variety of wild animals like bats, rats, civets, snakes, donkeys etc., are frequently sold and eaten as exotic delicacies. Bats and civets are found to be intermediate carriers for the zoonotic viruses before they were transmitted to human beings. Chinese authorities identified SARS-Covid-2 as the causative virus for the disease on 7 January 2020, and on 11 February 2020, WHO named the disease as Coronavirus disease 2019 (Covid-19). The outbreak was declared as global pandemic. Corona viruses are a family of viruses that cause infections in humans with symptoms like common cold and respiratory disorders. Sometimes influenza like symptoms can be misleading and prone to be ignored initially as it has around two weeks incubation time to manifest. The virus enters the human body through air droplets and travels through the respiratory track including mouth, nose, throat to attack lung tissues. The epithelial cells of lungs are primary targets. When body immune system cannot fight out the invasion by the virus, symptoms manifest in the body ranging from mild to severe respiratory Environmental Resilience and Transformation in times of COVID-19. DOI: 10.1016/C2020-0-02703-9

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3.  Impact of Covid-19 lockdown on the socioenvironmental scenario of Indian Sundarban

syndromes. Several independent research groups have identified that SARS-CoV-2 belongs to β-Coronavirus with a highly identical genome to bat coronavirus. Covid-19 virus uses the same receptor, Angiotensin-Converting Enzyme 2 (ACE2) as that of SARS-CoV, and mainly spreads through the respiratory tract. The clinical symptoms of Covid-19 include fever, cough, fatigue, and gastrointestinal disorders (Gautam and Trivedi, 2020). It has spread quickly across the globe through social mixing and personal contacts. Closely connected present day world facilitated quick spread of the disease. It is evident that how in a modern day globalized world connected by wide network of transport system and dominated by high speed air travel connecting countries and continents can spread a disease exponentially. The spread of Covid-19 infection through a social mixing is unique in history which was not experienced in case of SARS of the year 2002 and MERS of the year 2012 (da Costa et al., 2020). In India the disease is spreading at a rate of 1.60% (compound average growth) during January to May 2020, while the death rate is increasing at a rate of 1.58% (compound average growth; da Costa et al., 2020). Average death in respect to number of cases is 2.92%. In India, Maharashtra, Tamil Nadu, Delhi and West Bengal are the leading states in terms of total confirmed cases (Covid19-India, 2020). As the country is grappling with the Covid-19 outbreak remote islands located nearly 100 km away from Kolkata in the Sundarban area have managed to remain free of the virus for a long time. The Ghormara Island in South 24 Paraganas district’s Sagar block is yet to record single case of Covid-19. A visit to the island which has a population of about 3000 shows that life is going on as usual with shops, Panchayat office, post office, and ration outlets functioning as if nothing happened (Das, 2020). The socioecological processes of Sundarban is not only the resultant of the human activities of the local region, it is having the cumulative impact of the anthropogenic activates of entire southern Bengal region including Kolkata. The hypothesis associated with this study is that during Covid-19 Lockdown like other parts of the world, Indian Sundarban is also undergone socioenvironmental changes. To justify this hypothesis the present article is dealing with the impact of Covid-19 lockdown on the mangrove ecosystem of Indian Sundarban (Fig. 3.1). Though the prediction of this impact is quite difficult and can be comprehended after long term monitoring. In our study we tried to focus on the positive as well as the negative impact of Covid-19 lockdown on the socioenvironmental scenario of Indian Sundarban. Here discussion has been made on impact of Covid-19 lockdown on air, water and biodiversity of this area and also how people of Sundarban is affected due to labor migration and tourism related activities including movements of mechanized boats, use of loud speakers, disposal of wastes etc.

FIG. 3.1  Map showing location of Indian Sundarban (GoogleEarthPro, 2020).

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3.2  Impact on air and water quality

3.2  Impact on air and water quality Worldwide spread of Covid-19 in a quite short time has brought a dramatic decrease in industrial activities, road traffic and tourism. Restricted human interaction with nature during this crisis time has appeared as a blessing for nature and environment. Reports from all over the world are indicating that after the outbreak of Covid-19, environmental conditions including air quality and water quality in rivers are improving and wildlife is blooming. India has always been a hub of pollution with huge population, heavy traffics and polluting industries leading to high air quality index (AQI). But after declaration of lockdown due to Covid-19, quality of air has started to improve but other environmental parameters such as water quality of the rivers stared showing adverse effect. The levels of major air pollutants, including particulate matter sulfur dioxide, nitrogen dioxide, drastically reduced since lockdowns were enforced worldwide in response to the Covid-19 pandemic. In India, satellite data has shown a significant drop in particulate matter or aerosol levels after the Covid-19 lockdown over most parts of the country. Field measurements have shown that there is a substantial reduction in the concentration of suspended particulate matter (PM), a major component of air pollution, in the atmosphere due to reduced human activities as a consequence of the lockdown (Bera et al., 2020). The data of Diamond Harbour (22.1927° N, 88.1895° E) water quality of River Hooghly has been studied during lockdown period of 2020. The data was compared with the previous year data of same month which was worse than the lock down period of 2020 (Figs. 3.2 and 3.3). The pH and the dissolved oxygen (DO) level of both years were almost same in the same station. It is probably same solar radiation and rainfall in the both years. The dissolved oxygen is the outcome of primary productivity of the aquatic ecosystem which is controlled by solar radiation, diurnal variation, organic load of water body etc. (Yao et al., 2018). The other important pollution parameters are chemical oxygen demand (COD) and total solids (TS). The COD was lower in concentration in the previous year (Fig. 3.2) than the Covid-19 lockdown period. The other parameter like nitrate, phosphate and total dissolved solids were lower in the current year comparing to the previous year. This incident can be explained in this way that due to pandemic period use of detergent, hand sanitizer, antibacterial chemicals, plastic mask, gloves like hospital wastes are regularly being used in domestic sector also and they are directly disposed in the municipal waste and sewage system (Plate 3.1) which ultimately increase the COD level of the River Hooghly. The PPE wastes are getting disintegrated and increases the concentration of TS. Beside this the use of detergents in municipal areas increases the nutrient contents of river water. The other two pollution parameter of surface water are total coliform and fecal coliform load which have been significantly increased during lockdown period (Fig. 3.3). It is due to probably less human interference, closing of different festivals at religious place like temples, regular bathing, less pilgrim activities in the upper catchment of the river Ganges. A very recent study also reported that clean rivers and healthy

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FIG. 3.2  Chemical parameters (expressed in ppm except pH) at Diamond Harbour, West Bengal India (WBPCP, 2020). 1. Environmental modifications, degradation and human health risks

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3.  Impact of Covid-19 lockdown on the socioenvironmental scenario of Indian Sundarban

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Nov - 19

Oct - 19

Sep - 19

Aug - 19

Jul - 19

Jun - 19

May - 19

Apr - 19

0

Mar - 19

5000

FIG. 3.3  Chemical and microbial parameter at Diamond Harbour, West Bengal (WBPCP, 2020). (TSS and TDS are in PPM and total and fecal coliform are in number of organisms/mL.)

aquatic life symbolize that the ecosystem is functioning well. The river Ganges has shown signs of rejuvenation and a significant improvement on many parameters, following the eight-week nationwide lockdown due to coronavirus pandemic. Since industrial units and commercial establishments were closed, water was not being lifted by them with a negligible discharge of industrial wastewater. It was observed that during the lockdown period most of the districts in the Ganges basin observed 60% excess rainfall than the normal, which led to increased discharge in the river, further contributing toward the dilution of pollutants. Further, data analysis of live storages in the Ganges Basin revealed that the storage during the beginning of the third phase of lockdown was almost double than the storage during the same period of the previous year (Dutta et  al., 2020). The impact could be seen in terms of increased dissolved oxygen (DO) and reduced chemical oxygen demand (COD), fecal coliform, total coliform and nitrate (NO3-) concentration. A declining trend in nitrate concentration was observed in most of the locations due to limited industrial activities.

PLATE 3.1  Polluted water flowing from Kolkata to Sundarban during lock down period. 1. Environmental modifications, degradation and human health risks



29

3.2  Impact on air and water quality

140

120

100

PM10

PM2.5

NOx

SO2

CO

Ozone

80

60

40

20

0

Mar-20

Apr-20

May-20

Jun-20

Jul-20

Mar-19

Apr-19

May-19

Jun-19

Jul-19

standard

FIG. 3.4  Level of air pollutant (ppm) at Jadavpur, West Bengal (CPCB, 2020).

The air pollution data of the present and last year differ significantly in their concentrations. The last five month air pollution data of 2020 is much lesser than the previous year. The higher concentration of the ozone in March 2020 can be explained that it is the secondary pollutant and produces after photoreaction of automobile exhaust. Hence the elevated concentration of ozone is the results of initial pollution of the March 2020. Beside this the higher concentration of ozone particularly in March in both years depended on wind direction and number of sunny days. The concentration of PM10 at the particular location was higher at April 2020 is probably the scattered construction activities in surrounding areas in both years. Here we selected Jadavpur area because it is the nearest location to Sundarban, where West Bengal Pollution Control Board records the air pollution data round the year (Fig. 3.4). An Indian study aimed to examine the changes in air quality during different phases of the Covid-19 pandemic, including the lockdown and unlock period (postlockdown) as compared to prelockdown and to establish the relationships of the environmental and demographic variables with Covid-19 cases in the state of Maharashtra. Atmospheric pollutants such as PM2.5, PM10, NOx, and CO were substantially reduced during the lockdown and unlock phases with the greatest reduction in cities having larger traffic volumes. They compared data with the immediate prelockdown period, it was revealed the average PM2.5 and PM10 reduced by up to 51% and 47% respectively during the lockdown period, which resulted in “satisfactory” level of air quality index (Sahoo et al., 2021). The data of Sundarban also has shown the similar trends with compared to the National ambient air quality standard. A separate work recorded the CO2 level of nine locations in the Sundarban area in the month of April in current and previous year. Here also April 2020 showed lesser concentration of CO2 than the previous year (Mukherjee et al., 2020). The percentage reduction in the air CO2 level was in the following order: Jharkhali (22.05%) > Harinbari (21.45%) > Gosaba (20.76%) > Bali Island (20.57%) > Chotomollakhali Island (20.41%) > Chemaguri (16.62%) > Canning (16.22%) > Kachuberia (13.90%) > Sagar Lighthouse (13.09%) > Henry’s Island (12.04%). Overall, the results suggest that the Covid-19 induced global lockdown and decreased anthropogenic activities throughout the globe including the Indian state of West Bengal might have resulted in the reduction of air CO2 level (Mukherjee et al., 2020). Moreover, the indigenous thriving mangrove flora of the study sites might also act as key players in decreasing the initial atmospheric CO2 load (Fig. 3.5). Different types of solid wastes especially plastic wastes coming from different anthropogenic activities contaminated mangrove habitat of Sundarban during lock down period (Plate 3.2).

1. Environmental modifications, degradation and human health risks

30

3.  Impact of Covid-19 lockdown on the socioenvironmental scenario of Indian Sundarban

450 400 350 300 250 200 150

CO2 at April 2019

100

CO2 at April 2020

50 0

i i d ri ri ia al al gu nba slan er kh kh a b r i a I u l a m ar ol ch Jh H y’s he C om nr Ka t e H ho li

ng aba ni n os G Ca

Ba

C

FIG. 3.5  Carbon dioxide concentration (in ppm) in the air at different location of Sundarban (Mukherjee et al., 2020).

3.3  Natural regeneration of biodiversity The study site is the world’s largest delta and spreads over India and Bangladesh. The Indian part has one national park and five sanctuaries. Because of the threat of corona virus, entry of visitors to Sundarban and other national parks and sanctuaries in West Bengal was suspended from March 17, 2020 whereas the pan India lockdown started on March 25 in the same year. More than two lakh tourists, hundreds of honey collectors and fishermen enter Sundarban Tiger Reserve, which is the part of the entire mangrove forest, every year with permits. Tourist activities

PLATE 3.2  Plastic waste contaminating mangrove habitat of Sundarban during lock down period. 1. Environmental modifications, degradation and human health risks



31

3.3  Natural regeneration of biodiversity

500

Phytoplankton

450

Dissolved Oxygen

400 350 300 250 200 150 100 50 0 April 2018

April 2019

April 2020

FIG. 3.6  Phytoplankton (×10 /L) and dissolved oxygen (mg/L) status of Sagar Island Sundarban (Pal et al., 2020; WBPCP, 2020). 5

and entry of other people have been completely stopped. Patrolling on boats, the only means of monitoring the delta, has been increased to look out for tiger sighting as an indicator of Covid-19 lockdown on the dominating fauna of this region. The Border Security Force and police have also been asked to keep a watch. Before the lockdown, when tourists were allowed, authorities used to get reports of tiger sightings on not more than two days in a week. But since the lockdown started, patrolling teams are spotting tigers almost five to six days every week. On some days, reports of multiple tiger sightings from different areas of the tiger reserve are also coming from the forest guards (Thakur, 2020; WBFD, 2020). Another study reported that the number of the big cats at the UNESCO World Heritage site has also gone up to 96 from 88 in 2018 at the Indian part of the territory, according to the West Bengal state forest department reported latest headcount done on 7th May 2020. Officials counted as many as 43 female tigers and 11 cubs using over 700 pairs of all-weather night-vision camera traps (ET, 2020). Beside big animals the other aquatic organisms especially phytoplankton status has been varied significantly. An analytical result has shown that the standing stock of phytoplankton in the selected station was highest in April 2020 compared to the April 2019 and April 2020 (Fig. 3.6). The phytoplankton at the base of aquatic food pyramid are exposed to threats of various categories arising from industrial and domestic discharges. The suspended particulate matter and oil film associated with aquatic ecosystem inhibit the solar energy to penetrate the water column thereby posing a negative impact on phytoplanktons. This type of stress is common in the estuarine water of Indian Sundarbans due to continuous movement of passenger’s vessels, fishing trawlers, ships, oil tankers along the navigation route. In addition to this, the industries situated along the Hooghly estuary also add substantial amount of suspended particles in the water body thus retarding the growth of the tiny producer community. The pollutants from different anthropogenic activities impacted the life-cycle of the plankton community of these regions. The Covid-19 lockdown phase, however, turned down the picture of the environment (Pal et al., 2020). Due to lockdown imposed by the Central and State Government, the discharges from industries, tourism units have been cut-off. In addition, the water transport system has also ceased due to which the stress on this tiny producer community has been withdrawn. This is reflected through higher standing stock of phytoplankton during April, 2020 (430.63 ×105 organisms /L), compared to April, 2019 (226.75 ×105 organisms/L) and April, 2018 (219.03 ×105 organisms/L) as shown in Fig. 3.5. This increase in standing stock has high probability to accelerate the estuarine fish resources in the years to come. In conclusion it can be advocated that Covid-19 lockdown phase has accelerated the growth of phytoplankton species in the brackish water system along the Hooghly estuary, probably due to complete removal of stress posed by pollution from point and nonpoint sources (Pal et al., 2020). Thus, the Covid-19 lockdown process, in other way, has exposed the biodiversity of the aquatic ecosystems in a positive direction. The concentration of phytoplankton is partially correlated with dissolved oxygen of the aquatic system. The aquatic ecosystem in and around Indian Sundarbans has shown no exception to this rule. The DO level has exhibited two peaks during the entire data sets 2009 peak due to super cyclone Aila (Mitra and Zaman, 2015) and 2020 peak during Covid-19 lockdown phase (Pal et al., 2020). The recent peak may be due to minimal input of wastes from several anthropogenic sources that arise from industrial and domestic activities. The lockdown phase, initiated on and from 25th March, 2020 partially ceased all the industrial operations and movements of water transports that ultimately upgraded the 1. Environmental modifications, degradation and human health risks

32

3.  Impact of Covid-19 lockdown on the socioenvironmental scenario of Indian Sundarban

estuarine water quality as revealed by the hike in DO values. The increase of DO level has several positive implications particularly in the domain of sustaining the plankton community followed by fish resources of the estuarine system. The Indian Sundarbans boasts of around 172 species of fish, 20 species of prawn and 44 species of crabs, including two commercial species. They act as the nursery ground for nearly 90% of the aquatic species of eastern coast of India. The availability of important commercial species of the continental shelf that are harvested in India and neighboring countries is closely linked to the health of the Sundarbans. The Indian Sundarbans meet 15-20% of the requirement for fish in the eastern metropolis of Kolkata, the capital of the Indian state of West Bengal (Ghoshal et al., 2019). The positive impacts discussed above are all likely to be temporary, and it is currently not clear how the improvement in biological diversity has taken place as an aftermath of the pandemic. Noise, air, and water pollution, greenhouse gas emissions, and the other anthropogenic activities impacted on the mangrove ecosystem of Indian Sundarban but they were minimized during the Covid-19 lockdown period. On other side funding on wildlife conservation and research throughout the world has been compromised ultimately, conservation depends on boots on the ground and, if funding is limited, these activities will need to be prioritized. In this situation new funding and Government initiatives are required.

3.4  Migration of labor from other States Migration is the process by which individuals or whole households leave their usual place of residence for another geographic location, usually crossing an administrative or national border and remain for at least six months, usually as a result of a change in the relative attractiveness, real or perceived, of the usual place of residence with respect to the destination (Nicholls et al., 2017). Migration over past several decades from Bengal Delta has occurred due to better livelihood opportunities and earning better income. Such migrations however, are triggered by ability to have level of higher education or acquisition of vocational skills. Many educated men and women from interior of Bengal Delta especially from Indian Sundarban have long migrated to urban centers, taking opportunities for better prospects of earning. While the skilled rural workers (like plumbers) from Odisha are known to have migrated to West Bengal, over many decades, but such migration is mostly limited to the migration of the male member, who leave the families behind, send monthly remittances through postal services and visit the family back in the village at least three to four times a year (Bera, 2013; CMLS, 2014; SPREAD, 2009; Vasundhara, 2005). These migrations are not related to any climate induced phenomena but purely driven by economic needs and better job opportunities. Male migrants are more dominating than female migrants. Women take on the additional responsibilities of looking after the household and the farms in the absence of male members. Female migration received very little attention in the early migration theories, where women were either taken as dependent migrants or residuals. In early theory and literature female migrants were regarded as “associational migrants,” who moved along with their spouses or family (Mahapatro, 2010). It has been well known that women migrate more than men but only to shorter distances for working in domestic sectors, manufacturing industries, workshop etc. Similar trend can be witnessed in the case of upper estuarine part of Sundarban areas where the women migrate only to periurban areas of Kolkata, to work as domestic help and helper of construction work (CRS, 2010; Ghosh, 2012). The people of Sundarbans live in physically vulnerable circumstances which are cyclone prone, monsoonal and low-lying, with many settlements located alongside the waterways and coastline. The river embankments (3500 km app.) constructed in mid-19th century although in a very unscientific manner made possible to inhabit in the delta islands. The tides surges into the estuary system, pushing saline water over the embankments and through the breaches of embankments into agricultural fields causing serious damage to the lives and livelihoods of inhabitants. During postindependence period, this region witnessed sudden influx of population mainly due to migration. The displaced persons forcibly occupied vested lands and cleared forests for habitation (Samling et al., 2015). The rain fed agriculture is the mainstay of the economy in the Sundarban area, the socio economic profile is not uniform throughout the inhabited part of the area. This differentiation is due to geographical characteristics, population composition and their background, access to different sets of resources and subsequent occupational specialization, and the nonuniform pace of socio-economic transformation through the region. The main economic activity in the Sundarban delta, rain-fed paddy agriculture, is made possible by the construction of earthen embankments to keep brackish tidal water at bay. The people of this area are basically unskilled labor and migrate to other state of India for earning money (DasGupta and Shaw, 2015). 1. Environmental modifications, degradation and human health risks

References 33

People of Sundarban region has experienced unemployment, loss of local resources due to natural calamities, increased salinity of soil, fresh water crisis, human-tiger conflict but never faced socioeconomic crisis raised due to Covid-19 lockdown situation. The people of this area are engaging in the different professions in different seasons. Both and female had to migrate specially after cyclone Aila which was held in the year 2009 to the nearby city and other states. They were mostly engaged in the work of construction labor, worker in cotton mill and other industries etc. Most of these workers had to returned back to their own village from the different states like Maharashtra, Karnataka, Kerala, Tamilnadu, Rajasthan, Delhi, Uttar Pradesh etc. The coronavirus disease (Covid-19) has spread to Bengal’s rural areas with nearly 200 migrant workers who recently returned from other states testing positive for the infection, and the numbers likely to rise as more arrive (Bhattacharya, 2020). The trend is similar to that noticed in Bihar, Odisha, Uttar Pradesh, and Jharkhand, the other states where a large number of migrant workers have returned from more affluent states such as Karnataka, Gujarat, Maharashtra and Punjab (Chowdhury et al., 2020; Gupta et al., 2020). Newspaper has reported on 27th April, 2020 (TOI, 2020) that residents of Mousuni, who had been working as a mason in Kerala, had to return their own home within few days as the Corona virus started to spread in Kerala since last week of January,2020, around mid-March all work stopped and the contractor asked the labor group of about 200 laborers from Mousuni, Sagar and other areas of the Sundarbans to return home by end of April, 2020. They had to vacate their rooms within hours, were forced to change three trains, they took a bus in between and finally crossed the Hooghly (the main western distributary of the Ganga) from Haldia to reach home. The journey was a three-day continuously (TOI, 2020). A few of them went to the hospital for Covid test when they got off the train, and before boarding a ferry to cross the Hooghly. The doctors gave medicines to those who had symptoms of Covid-19. Over 3000 migrant workers returned to various villages in the Gosaba area in the wake of the Covid-19 outbreak; almost none had a check-up. At a conservative estimate, around 30% of the families living in the Sundarbans have at least one member working outside West Bengal, which means 250,000–300,000 migrant workers who had return back to Indian Sundarban region (Basu, 2020).

3.5 Conclusion The Covid-19 pandemic is impacting all segments of human society. Scientific community are trying hard to understand that how the pandemic will affect the different spheres of the earth. The biosphere is the real concern of the present pandemic where in India near about 1.2 Lakhs of people died within last 8 months. It is also important to understand the effect of Covid-19 lockdown on the sensitive ecosystem like Sundarban mangrove in India and to find out the degree of impact on this ecosystem. How is the pandemic affecting Sundarban’s environment now? It is too early for a true answer. The first national lockdown has improved the air quality of the nearby region of Indian Sundarban which was probably due to the restriction of transportation and industrial activities. The local transportation, movement of boats and tourism activity were closed after declaration of lockdown as a result frequent tiger sighting was reported. This lockdown situation is good for the wildlife of this area. The quality of surface water was not so good specially with respect to COD values, it is probably due to excessive use of chlorinated and alcohol based disinfectant, soap and detergent from upper catchment of river Hooghly. There may be a big chance of retention of those residual chemicals in the mangrove habitats of Sundarban for a longtime which will be detrimental for the ecosystem. The canals carrying waste water toward Sundarban were full of froths and the color of water has changed during the lockdown period reflecting the enhancement of water pollutant. The ultimate struggle of people of Sundarban for food has been enhanced after lockdown due to returning back of workers from other states of India. Still now anthroposphere of the area has been suffering of unemployment, joblessness and fear of new Noble Corona virus coming from the urban area during unlocking phase. The ecosystem will reach their cybernetics whenever public transportation like local train service will reopen smoothly and mankind will be relieved after availability of the Covid-19 vaccine. Hence it can be concluded that Covid-19 lockdown has imposed positive impact on local air quality and biodiversity, in contrary it has created negative impact on water quality and scocioeconomy with special reference to labor migration and tourism activity.

References Bakshi, M., Chaudhuri, P., 2014. Antimicrobial potential of leaf extracts of ten mangrove species from Indian Sundarban. Int. J. Pharma Bio Sci., 5 (1), 294–304. Basu, J., 2020. People Rush Back to the Sundarbans, Untested. Retrieved from https://www.thethirdpole.net/2020/03/31/people-rush-backto-the-sundarbans-untested/ on 31st October 2020.

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Bera, B., Bhattacharjee, S., Shit, P.K., Sengupta, N., Saha, S., 2020. Significant impacts of COVID-19 lockdown on urban air pollution in Kolkata (India) and amelioration of environmental health. Environ. Dev. Sustain. 22, 1–28. doi:10.1007/s10668-020-00898-5. Bera, M.K., 2013. Environmental refugee: a study of involuntary migrants of Sundarban Islands, in: Paper Presented at the Proceedings of the Seventh International Conference on Asian and Pacific Coasts (APAC 2013), Bali, Indonesia, September 24–26, 2013. Bhattacharya, B.D., Nayak, D.C., Sarkar, S.K., Biswas, S.N., Rakshit, D., Ahmed, M.K., 2015. Distribution of dissolved trace metals in coastal regions of Indian Sundarban mangrove wetland: a multivariate approach. J. Clean. Prod., 96, 233–243. Bhattacharya, S., 2020. Returning migrants cause surge of Covid-19 cases in rural Bengal. Hindustan Times, Kolkata. Retrieved from https:// www.hindustantimes.com/india-news/returning-migrants-cause-surge-of-covid-19-cases-in-rural-bengal/story-2wXPrIdPWNIpAXBLbRs3eM. html on 1st December 2020. Biswas, S.R., Biswas, P.L., Limon, S.H., Yan, E.-R., Xu, M.-S., Khan, M.S.I., 2018. Plant invasion in mangrove forests worldwide. For. Ecol. Manage., 429, 480–492. Census of India, 2011. Government of India. Chaudhuri, A., Choudhury, A., 1994. Mangroves of the Sundarbans, Vol. 1. India: IUCN, Bangkok, Thailand. Chowdhury, A., Nath, R., Rozencranz, A., 2020. The Sunderbans in crisis. Econ. Polit. Wkly., LV (34), 5. CMLS, 2014. Stories and a canvas: seasonal labor migration and migrant workers from Odisha. In: Centre for Migration and Labor Solutions. Aajeevika Bureau, Udaipur, India. Covid19-India, 2020. Coronavirus Outbreak in India. Retrieved from www.covid19india.org on 12th August 2020. CPCB, 2020. Central Control Room for Air Quality Management. Retrieved from https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/ caaqm-landing/data on 16th August 2020. CRS, 2010. Study on Migration as a Coping Mechanism in the Context of Cyclone Aila and Its Impact on the Lives of the People in South 24 Parganas District of West Bengal, India. Catholic Relief Services, World Wildlife Fund – India, Association of Bengal Collaborators for Development, Palli Unnayan Samity –Baruipur. da Costa, V.G., Moreli, M.L., Saivish, M.V., 2020. The emergence of SARS, MERS and novel SARS-2 coronaviruses in the 21st century. Arch. Virol., 165 (7), 1517–1526. doi:10.1007/s00705-020-04628-0. Das, S., 2020. Sundarban Island in West Bengal still free from Covid-19. Deccan Herald. Retrieved from https://www.deccanherald.com/ national/sundarban-island-in-west-bengal-still-free-from-covid-19-897750.html on 29th October, 2020. DasGupta, R., Shaw, R., 2015. 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Blue Carbon Reservoir of the Blue Planet. Springer, India. Mukherjee, P., Zaman, S., Mitra, A., 2020. Covid-19 induced lockdown caused a reduction in atmospheric carbon dioxide level in the mangrove ecosystem of Indian Sundarbans: a spatio-temporal picture. In: Mitra, A., Monruskin, M.C., Chakrabarty, S.P. (Eds.), Natural Resources and Their Ecosystem Services—Webinar Proceeding on ‘Ecosystem Services and United Nations Sustainable Development Goals’ Celebrating the World Environment Day 5th June 2020, 153–160. Nicholls, R., Kebede, A.S., Allan, A., Arto, I., Cazcarro, I., Fernandes, J.A., …, Whitehead, P., 2017. The DECCMA Integrated Scenario Framework: A Multi-Scale and Participatory Approach to Explore Migration and Adaptation in Deltas. DECCMA Working Paper, Deltas, Vulnerability and Climate Change: Migration and Adaptation, IDRC Project Number 107642. Retrieved from www.deccma.com on 31th October, 2020. Pal, N., Barman, P., Das, S., Zaman, S., Mitra, A., 2020. Status of brackish water phytoplankton during Covid-19 lockdown phase. NUJS J. Regul. Stud. Special Issue on COVID 19, 83–86. Pattanaik, C., Reddy, C., Dhal, N., Das, R., 2008. Utilisation of mangrove forests in Bhitarkanika wildlife sanctuary, Orissa. Indian J. Trad. Knowl. 7 (4), 598–603. Saenger, P., 2002. Mangrove Ecology, Silviculture and Conservation. Springer Science & Business Media, Dordrecht, Netherlands. Sahoo, P.K., Mangla, S., Pathak, A.K., Salãmao, G.N., Sarkar, D., 2021. Pre-to-post lockdown impact on air quality and the role of environmental factors in spreading the Covid-19 cases-a study from a worst-hit state of India. Int. J. Biometeorol. 65 (2), 205–222. Samling, C. L., Das, S., & Hazra, S. (2015). Migration in the Indian Bengal Delta and the Mahanadi Deltaa review of the literature, DECCMA Working Paper, Deltas, Vulnerability and Climate Change: Migration and Adaptation, IDRC Project Number 107642. Retrieved from www. deccma.com on 31st October, 2020.

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SPREAD, 2009. Migration in Koraput. Society for Promoting Rural Education and Development, Koraput, Odisha. Tack, J., Polk, P., 1999. The Influence of Tropical Catchments Upon the Coastal Zone: Modelling the Links Between Groundwater and Mangrove Losses in Kenya, India/Bangladesh and Florida. John Wiley & Sons Ltd, Chichester, UK. Tam, N., Wong, Y., 1999. Mangrove soils in removing pollutants from municipal wastewater of different salinities. J. Environ. Qual., 28 (2), 556–564. Thakur, J., 2020, 13th April. Tiger Sightings Increase in the Sunderban With Lockdown But No Tourists to See Them. Hindustan Times, Kolkata. Retrieved from https://www.hindustantimes.com/india-news/tiger-sightings-increase-in-the-sunderban-with-lockdown-but-notourists-to-see-them-amid-covid-19/story-c0dWqJe7k1efUzTRV6F0VL.html on 29th October, 2020. TOI, 2020. Sunderbans Islands Cut Off After 3 Cases in Mainland. Times of India. Retrieved from https://timesofindia.indiatimes.com/city/ kolkata/2-sunderbans-islands-cut-off-after-3-cases-in-mainland/articleshow/75397239.cms on 31st October, 2020. Van Lavieren, H., Spalding, M., Alongi, D.M., Kainuma, M., Clüsener-Godt, M., Adeel, Z., 2012. Securing the Future of Mangroves. United Nations University, Institute for Water, Environment and Health, Hamilton, Canada. Vasundhara, 2005. Development Policies and Rural Poverty in Orissa: Macro Analysis and Case Studies. Planning Commission, Government of India Bhubaneswar, Bhubaneswar. WBFD, 2020. Forests of West Bengal. West Bengal Forest Department. Retrieved from http://www.westbengalforest.gov.in/ on 15th August 2020. WBPCP, 2020. Water Quality Information System. Retrieved from http://emis.wbpcb.gov.in/waterquality/viewsampledatacitizen.do on 16th August, 2020. Yao, Y., Wang, X., Li, Y., Wang, T., Shen, M., Du, M., …, Piao, S., 2018. Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years. Global Change Biol., 24 (1), 184–196. doi:10.1111/gcb.13830.

1. Environmental modifications, degradation and human health risks

C H A P T E R

4 Changes in nighttime lights during COVID-19 lockdown over Delhi, India Asmita Deepa,b, Prasun Kumar Guptaa a

Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO), Dehradun, India b Faculty of Geoinformation Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands

4.1 Introduction Conventional studies analyzing the electricity sector considers the consumption profile with respect to direct factors only. These factors affect the rate and cost of production of electricity and its consumption based on commercial, domestic, and essential demands. During COVID-19 crisis, these analyses fail to be relevant because impact of COVID lockdown on commercial EPC is not considered. Also, the factors like reduction in societal activities & increase in stay-at-home population during COVID lockdown were not taken into account. There exists multidomain correlation between various factors like stay-at-home population, lockdown of commercial and industrial sectors, COVID-19 cases and deaths reported, mobility reported in various sectors, etc. When these factors are considered cumulatively, they can give insights into the impact on electricity sector during the lockdown. A similar approach has been followed for analyzing the short-run impact of COVID-19 on the U.S. electricity sector (Ruan et al., 2020). This is a new approach in the Indian scenario and very few attempts analyzing the impact of COVID-19 on the electricity sector of India has been made following this cross-domain approach. Nighttime lights (NTL) has been used in several studies as a proxy measure for electricity consumption or to predict energy distribution in an area (Falchetta et al., 2019, 2020; Falchetta & Noussan, 2019; Román, Stokes, et al., 2019; Ruan et  al., 2020). The spread of infectious diseases and the factors responsible in triggering the spread have also been explored in some studies using NTL imagery as a measure to monitor the human mobility and its effects on the morbidity rates (Donalisio et al., 2020; Lai et al., 2019; Small et al., 2020; Tizzoni et al., 2014; Zhao et al., 2019). The referred studies have used NTL, individually, as a proxy measure for electricity consumption and human mobility for assessing spread of infectious diseases. The use of multidimensional correlation, in our study, for analyzing the impact of COVID19 on electricity sector cumulatively using NTL, is a new approach, especially in the Indian scenario. Román et  al. (2018) introduced NASA’s Black Marble NTL product suite (VNP46A1) which was developed to realize the full potential of the VIIRS DNB time series data. At 500 m spatial resolution and a temporal resolution of 1 day, the dataset provides global coverage of corrected NTL within 3–4 hours of acquisition. The daily NTL provided by VNP46A1 is enhanced and corrected for various atmospheric, terrain, vegetation, snow, lunar, and stray light variations and other intrinsic surface optical properties (Román et al., 2019). The corrections made for nonlinear changes in phase and libration using lunar irradiance model, for atmospheric and bidirectional reflectance distribution function (BRDF) effects, for seasonal vegetation variations in NTL and for temporal data gaps, makes it a current state-of-the-art for NTL applications. This study aims to monitor the changes in the NTL using NASA’s Black Marble Product Suite (VNP46A1) during COVID-19 lockdown over Delhi, India. It focusses on using NTL as a proxy measure to study electricity consumption and how it has been impacted during lockdown. Along with NTL, multidomain datasets like number of cases confirmed and deceased, reported due to COVID-19, mobility data in various sectors and daily electric power Environmental Resilience and Transformation in times of COVID-19. DOI: 10.1016/C2020-0-02703-9

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Copyright © 2021 Elsevier Inc. All rights reserved.

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4.  Changes in nighttime lights during COVID-19 lockdown over Delhi, India

consumption (EPC) dataset have been considered to study the impact on the electricity sector due to other factors prevailing amid COVID-19 crisis.

4.2  Study area and data used Delhi, containing New Delhi, the capital of India, has been considered as the study area. It is located at the centroid coordinates of 28°38′47.4” N, 77°6′32.04” E. The location map of study area is shown in Fig. 4.1. Delhi has been identified as one of the major hot spots of COVID-19 in India with over 174,748 confirmed cases and 4,444 deaths as reported on August 31, 2020 (COVID19 Statewise Status, 2020)​. The following datasets have been used in our study: 1. NTL data—The NASA’s Black Marble Product Suite (VNP46A1) for the image tile H25V06 containing Delhi is taken for the months of March, April, and May of 2019 and 2020 available on a daily basis at 500m spatial resolution (NASA LAADS DAAC VNP46A1-5000, 2019). The processed data is available within 3–5 hours of acquisition in HDF5 format from which the raster layers containing radiance at sensor and quality flags information has been extracted and saved as GeoTiff for further processing and analysis. 2. Mobility data—The community mobility dataset created by using location information provided by Google (Google COVID-19 Community Mobility Reports, 2020), highlights the percentage change from a baseline in mobility at places like grocery stores, transit stations, workplaces, parks and residential areas. The baseline is created considering the median value, for the corresponding day-of-week, during the five-week period January 3 – February 6, 2020. The data is present in CSV format, on a daily basis at state level for different countries out of which Delhi, India data is used for the period of March, April, and May, 2020. 3. COVID-19 data—The API made available by covid19india.org (COVID-19 India API, 2020), is used to extract state-wise daily data of the number of COVID-19 confirmed and deceased cases (per day) for Delhi and saved

FIG. 4.1  Location map of study area. 1. Environmental modifications, degradation and human health risks



4.3 Methodology

39

in CSV format for further processing. The data is available from Mar 14, 2020 onwards till the time 7 cases for Delhi were reported with no deaths. 4. Electricity Consumption data—The data is collected from the Power System Operation Corporation Limited (POSOCO), which is a Government of India (GoI) owned enterprise under the Ministry of Power (Daily Reports - National Load Dispatch Centre, POSOCO, 2020). POSOCO ensures the integrated and reliable operation of India’s grid. The daily reports of electricity consumption are made available with a delay of one day in pdf formats. While the total electricity consumption in these documents is not broken down between different uses like residential, commercial, etc., but it provides the daily EPC data in Mega Watts (MW) at the state-level. The daily pdf documents were downloaded and exported as CSV using the Python library Tabula.

4.3 Methodology The flowchart (see Fig. 4.2) represents the stepwise methodology adopted in our study. The NTL data from NASA’s Black Marble product is taken as input. The radiance at sensor and Quality Flag (QF) layers (Román et al., 2019) are extracted as GeoTiff files. Next, the steps of clipping the rasters to study area and scaling is done and unambiguous

FIG. 4.2  Flowchart depicting the methodology adopted in this study.

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4.  Changes in nighttime lights during COVID-19 lockdown over Delhi, India

pixels are set to NODATA. A threshold of 1.5 nW.cm−2.sr−1 is applied and pixels below this threshold are set to NODATA to remove aurora and temporary lights. The processed NTL data is thus obtained. The EPC data is incorporated with the NTL data and its pre-processing is done. In the next step, the correlation between EPC and NTL for COVID-19 lockdown months from March 25 to May 31, 2020 is obtained and compared with the correlation between these two for the same time period for the previous year, i.e. March 25 to May 31, 204. For this mean of lights is taken to bring NTL to state level, ignoring NODATA values. Taking mean of lights is one of the popular methods for aggregation of NTL. Other previously used methods are sum of lights (SOL) (Elvidge, et al., 2009), median of lights, etc. The next step is the incorporation of other datasets which are mobility and COVID cases. The creation of dayof-week baselines to bring NTL and EPC data at par with the mobility data is done. The baseline is created considering the median value, for the corresponding day-of-week (Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, and Saturday), during the five-week period January 3–February 6, 2020. Using this, seven NTL images for each day-of-week for five consecutive weeks are stacked together. The percentage deviation from baseline is calculated for each NTL image from March 1 to May 31, 2020. The median of lights is taken to bring NTL to state level. Similarly, for EPC data, present as daily lumped quantity, the respective day-of-week baseline values are calculated and then expressed as percentage deviation from baseline. The COVID cases are taken as absolute values here. Thus, the multidomain dataset during COVID-19 lockdown months is obtained as a single merged dataframe. A dataframe is a 2-D data structure where data is aligned in row and column format. Here, a dataframe is formed by merging the multidomain datasets using Pandas package in Python. The moving average with a window size of 12 days is taken for these datasets. This value is taken because the incubation period of COVID19 is 2–14 days which means that symptoms of COVID-19 may appear within 12 days of exposure to the virus (Centres for Disease Control and Prevention, 2020). The moving averages help to clear out the noise in data from day-to-day fluctuations and smoothens it. This gives a clear view of trends present in the data and makes the analysis easier. Next, the correlation between these multidomain datasets is obtained and studied. A symbolical regression-based approach for obtaining a model equation of EPC depending on the said variables is followed (Schmidt & Lipson, 2009). The insights from the model equation for predicting future trends in EPC are discussed.

4.4  Results and discussion This section is subdivided into four sub-sections including (i) exploration of individual datasets, (ii) results of comparison of correlation between NTL and EPC with previous year, (iii) results of correlation of the multi-domain datasets during COVID-19 lockdown, and (iv) symbolical regression-based approach for predicting EPC and discussion on the insights.

4.4.1  Exploration of individual dataset 4.4.1.1  COVID-19 dataset The COVID-19 dataset containing the number of cases confirmed and deceased per day during COVID-19 lockdown period from March to May, 2020 have been displayed in Fig. 4.3. The dataset contains the reported number of confirmed and deceased COVID cases per day. The data is available from March 14, 2020 onwards until then seven confirmed COVID cases with one death were reported. A sudden increase in the confirmed cases after March 25, 2020, when the lockdown was imposed as the symptoms of COVID19 are seen after 2–14 days of exposure to virus. The deaths reported are very less as compared to the confirmed cases. The irregularity in the data could also be due to nonreporting, underreporting, or misreporting of cases, overwhelmed public health infrastructure, and intermediate strictness measures taken by the state government. 4.4.1.2  Mobility dataset The dataset shows mobility as percentage deviation from the baseline in the six sectors namely—Retail and Recreation, Grocery and Pharmacy, Parks, Transit Stations, Workplaces, and Residential (see Fig. 4.4). The first dip in the dataset on March 10, 2020 shows decreased mobility in all other sectors whereas an increased mobility in residential areas. This might be due to National holiday for the festival of Holi in India. The next dip is seen on March 25, 2020 when the lockdown was imposed in the whole nation. The mobility in commercial sectors has shown a decrease

1. Environmental modifications, degradation and human health risks



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4.4  Results and discussion

COVID cases reported per day (Delhi) No. of COVID cases per day

1400 1200 1000 800 600 400 200 30–May–20

20–May–20

10–May–20

30–Apr–20

20–Apr–20

10–Apr–20

31–Mar–20

21–Mar–20

11–Mar–20

1–Mar–20

0

Date Confirmed Cases

Deceased Cases

FIG. 4.3  COVID cases, confirmed and deceased, reported per day from March to May, 2020 (Delhi).

from baseline whereas mobility in residential areas has shown an increase, which is due to increase in stay at home population during lockdown. Our analysis is mainly focused from March 25, 2020 onwards to study the correlation of mobility with other variables considered. The data shows gradual increase in mobility under the category “Transit Stations”; this confirms the news articles on post lockdown mass movements of low- and middle-income workers and laborers back to their hometowns. Several states allowed markets to be opened on certain days of the week, which is seen in the spikes in the “Retail and Recreation” category. A marked dip is seen around April 15, 2020 in the “Parks” category, which clearly represents the extension of lockdown to the next phase by the central government with more stringent restrictions on going out to nearby common places like parks, etc. Two marked jumps can be seen in “Grocery and Pharmacy” sector after April 30, 2020 and next around May 20, 2020. These might be due to the increase in mobility observed after giving conditional relaxations to essential places like medical and grocery stores in the regions where the spread was contained. The jumps and dips in “Workplaces” and “Residential” categories respectively show a complimentary trend which corresponds to the fact that attendance was made compulsory once a week in various government offices and other workplaces during the lockdown months.

Mobility as percentage deviation from baseline (Delhi)

Retail and Recreation

Grocery and Pharmacy

Transit Stations

Workplaces

30–May–20

20–May–20

10–May–20

30–Apr–20

Date

Parks

20–Apr–20

10–Apr–20

31–Mar–20

21–Mar–20

11–Mar–20

60 40 20 0 –20 –40 –60 –80 –100 –120 1–Mar–20

Mobility (percentage deviation from baseline)

4.4.1.3  NTL Dataset The NTL data as expressed as percent deviation from baseline has been shown in Fig. 4.5. The moving averages with a window size of 12 days is taken and median of lights is considered to bring NTL to state level. The night

Residential

FIG. 4.4  Mobility as percentage deviation from baseline (Delhi). The various sectors involved are: mobility at - retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential.

1. Environmental modifications, degradation and human health risks

42

4.  Changes in nighttime lights during COVID-19 lockdown over Delhi, India

100 50 0 –50

30–May–20

Date NTL percentage deviation

20–May–20

10–May–20

30–Apr–20

20–Apr–20

10–Apr–20

31–Mar–20

21–Mar–20

11–Mar–20

–100 1–Mar–20

NTL (percentage deviation from baseline)

NTL as percentage deviation from baseline (Delhi) 150

12 per. Mov. Avg. (NTL percentage deviation)

FIG. 4.5  NTL expressed as percentage deviation from baseline (Delhi). The moving averages with a window size of 12 days is taken and median of lights is considered to bring NTL to state level.

before the festival of Holi, there is a widely followed religious practice of lighting bonfires. This can be seen in the peak on March 9, 2020. The deviations in NTL shows dips which appear to be inconsistent with any of the mobility categories. However, similar to most mobility categories, a negative trend is also observed in the NTL dataset. This can be clearly seen in the moving averaged data. A similarity in the NTL trend can be seen with the mobility in residential areas after lockdown was imposed. Fig. 4.6 shows how percentage deviation in NTL from baseline has varied with respect to the day-of-week during COVID-19 lockdown months. It shows that there is more positive deviation on Monday and less negative deviation on Friday. This implies that NTL captured during lockdown months shows greater negative deviation on weekends especially Friday because of restrictions and shut down of commercial places. 4.4.1.4  EPC Dataset The EPC (in MW) data from POSOCO during COVID-19 lockdown period has been plotted with respect to date as shown in Fig. 4.7. The dataset provides the daily EPC in Mega Watts (MW) at the state-level. A dataset giving breakup of total EPC into different sectors like residential, commercial etc. is unavailable. The available dataset has been used in the comparison with absolute NTL values. The EPC data expressed as percent deviation from baseline

% deviation in NTL from baseline

NTL percentage deviation with day–of–week during lockdown 100 75 50 25 0 –25 –50 –75

y da

n Su

y da

M

on

y da

es Tu

y da

ed W

s ne

d rs hu

T

ay

ay

id Fr

ay rd

tu Sa

day of week FIG. 4.6  NTL percentage deviation from baseline w.r.t day-of-week during lockdown. 1. Environmental modifications, degradation and human health risks



43

4.4  Results and discussion

Electric Power Consumed per day from POSOCO (Delhi)

EPC per day (MW)

6000 5000 4000 3000 2000 1000

30–May–20

20–May–20

Date

10–May–20

30–Apr–20

20–Apr–20

10–Apr–20

31–Mar–20

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1–Mar–20

0

EPC (in MW)

FIG. 4.7  EPC per day (in Delhi) during lockdown months.

(January 3–February 6, 2020) has also been shown in Fig. 4.8, which has been used in the correlation with other variables. Due to increased EPC in the winter season (January–February; used to generate the baseline), the reduced EPC in March–May (summer season) appear as negative, as shown in the negative values of percentage deviation of EPC from baseline. The EPC deviation data shows consistency with the mobility datasets as the first dip is shown on March 10, 2020. The further trends after lockdown was imposed, i.e. March 25, 2020 are also similar. The EPC although does not show much similarity with the trends in NTL data. To get more insights on the correlation between each variable, Pearson’s correlation coefficient, R is calculated pairwise.

4.4.2  Comparison of correlation between NTL and EPC with previous year The correlation between average radiance values of NTL and the EPC data (in MW) has been studied during COVID19 lockdown period and has been compared with that of the same months, i.e. March 25 to May 31 for 2019. This is done to study the effect of COVID-19 on the correlation of the two variables compared to previous year. This study is done with absolute values of NTL and EPC (the percentage deviation or moving average values have not

30–May–20

Date EPC percentage deviation

20–May–20

10–May–20

30–Apr–20

20–Apr–20

10–Apr–20

31–Mar–20

21–Mar–20

11–Mar–20

1–Mar–20

EPC (percentage deviation from baseline)

EPC as percentage deviation from baseline (Delhi) 30 20 10 0 –10 –20 –30 –40 –50 –60

12 per. Mov. Avg. (EPC percentage deviation)

FIG. 4.8  EPC expressed as percent deviation from baseline (Delhi). The moving averages with a window size of 12 days is taken for EPC data. 1. Environmental modifications, degradation and human health risks

44

4.  Changes in nighttime lights during COVID-19 lockdown over Delhi, India

Correlation between average NTL radiance & EPC for March 25 to May 31, 2019 (Delhi) R = –0.89

EPC 2019 (in MW)

7000 6000 5000 4000 3000 2000 1000 0

0

5

10

15

20

25

30

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40

Average NTL Radiance 2019 (in nW.cm–2.sr–1)

(a)

Correlation between average NTL radiance (range: 0–20) & EPC for March 25 to May 31, 2019

(b)

7000

EPC 2019 (in MW)

EPC 2019 (in MW)

6000

5000 4000 3000 2000 1000 0

R = –0.70

7000

R = +0.89

6000

Correlation between average NTL radiance (range: 20–40) & EPC for March 25 to May 31, 2019

5000 4000 3000 2000 1000

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Average NTL Radiance 2019 (in nW.cm–2.sr–1)

0

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Average NTL Radiance 2019 (in nW.cm–2.sr–1)

FIG. 4.9  Correlation of NTL and EPC for 2019 with radiance values categorized as (a) full range 0-40 (b) range 0-20 (Residential) (c) 20-40 (Transit stations, parks, workplaces, and other commercial sectors).

been considered here). The Fig. 4.9 shows high negative correlation between average NTL radiance and EPC data for 2019 for Delhibut if we split the NTL data and consider the range of NTL radiance values from (a) 0–20 nW.cm−2.sr−1 and (b) 20–40 nW.cm−2.sr−1, it clearly shows a separate relationship between NTL and EPC considering the NTL radiance threshold value of 20 nW.cm−2.sr−1. This threshold matches with the threshold value considered by Liu et al., 2020. He has mentioned three different ranges of NTL radiance as 5–20, 20–40, and greater than 40 (nW.cm−2.sr−1) which are appropriate radiance ranges for residential areas, transportation and public facilities and commercial centres, respectively. In Fig. 4.9a, there seems to be high positive correlation for NTL < 20 implying that low lit areas, particularly residential areas, have positive correlation with EPC for the year 2019, whereas, highly lit areas (transit stations, parks, workplaces and other commercial sectors like retail and recreation, and grocery and pharmacy) have negative correlation with EPC (Fig. 4.9b). The high magnitude of Pearson’s coefficient shows high correlation of EPC with highly lit areas, although a negative sign could be because the EPC data taken is a cumulative figure. EPC data with breakup of total consumption into different categories (as mentioned in Section 4.2) can give better insights on the correlation. But for the year 2020, during COVID-19 lockdown months, the correlation between NTL and EPC in both ranges for NTL < 20 and NTL > 20 shows a weak negative correlation as shown in Fig. 4.10. The decrease in the magnitude of correlation in 2020 can be attributed to the effect of COVID-19 lockdown. Because of the nationwide lockdown, most of the commercial sectors were shut down which has highly affected the total EPC in the region. The less dependence of EPC on NTL as compared to previous year can also be attributed to the increase in dependence of EPC on other factors like COVID cases reported in that region, mobility observed in various sectors etc. This has clearly been reflected from our study by correlating the multidomain datasets with EPC along with NTL during lockdown period. Hence, it can be concluded that the total EPC in the region is not directly dependent entirely on NTL but is also influenced by factors arising due to COVID-19 in the year 2020. Thus, a complex relationship between EPC and NTL along with other factors related to COVID-19 prevails for the lockdown scenario.

4.4.3  Results of correlating the multi-domain datasets during COVID-19 lockdown The correlation between various variables considered for our study is calculated using Pearson’s correlation method. For this the percentage deviation from baseline is considered for mobility, NTL and EPC data whereas absolute values of COVID cases data are taken. The moving averages of these datasets is considered with a window size of 1. Environmental modifications, degradation and human health risks



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4.4  Results and discussion

EPC 2020 (in MW)

Correlation between average NTL radiance & EPC for March 25 to May 31, 2020 (Delhi)

R = −0.55

6000 5000 4000 3000 2000 1000 0

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Average NTL Radiance 2020 (in nW.cm−2.sr−1)

EPC 2020 (in MW)

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Correlation between average NTL radiance (range: 0–20) & EPC for March 25 to May 31, 2020 R = −0.13

5000 4000 3000 2000 1000 0

(b)

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Correlation between average NTL radiance (range: 20–40) & EPC for March 25 to May 31, 2020 R = −0.30

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EPC 2020 (in MW)

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Average NTL Radiance 2020 (in nW.cm−2.sr−1)

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Average NTL Radiance 2020 (in nW.cm−2.sr−1)

FIG. 4.10  Correlation of NTL and EPC for 2020 with radiance values categorized as (a) full range 0-40 (b) range 0-20 (Residential) (c) 20-40 (transit stations, parks, workplaces, & other commercial sectors).

12 days. The pairwise correlation between each variable gives insight on how strong or weak the association between the variables is. The results of R ranging from -1 to 1 have been tabulated in Table 4.1. The main inferences are: • There is a strong positive correlation of 0.786 between percentage deviation from baseline in NTL and mobility seen in parks. This might be because luminance in parks and common areas directly correlates to human mobility in these areas. With increased stay-at-home practices, lighting of parks and common areas for safety of children, women and families strolling at night, could be a reason. • The NTL shows weak positive correlation with the mobility in residential areas which is obvious as the indoors lights are not usually a good measure of NTL captured from the satellite imagery. • The deviation in EPC has shown high positive correlation with deviation in mobility at workplaces and transit stations. During the lockdown, the mobility in these areas was highly restricted, therefore mobility and EPC have both reduced drastically; leading to a higher positive correlation value. TABLE 4.1  Pairwise Correlation between each variable using Pearson’s method Grocery and pharmacy Parks

Pearson’s correlation, R

Confirmed Deceased NTL Retail and cases cases deviation recreation

Confirmed cases

1.000

Deceased cases

0.756

1.000

NTL deviation

−0.605

−0.406

1.000

Retail and recreation

0.800

0.722

−0.281

1.000

Grocery and pharmacy 0.891

0.694

−0.622

0.848

1.000

Transit stations

EPC Workplaces Residential deviation

Parks

−0.404

−0.138

0.786

0.007

−0.499

1.000

Transit stations

0.895

0.705

−0.619

0.873

0.997

−0.466 1.000

Workplaces

0.891

0.725

−0.567

0.922

0.977

−0.362 0.989

1.000

Residential

−0.848

−0.715

0.459

−0.957

−0.935

0.227

−0.986

1.000

EPC deviation

0.886

0.661

−0.727

0.768

0.982

−0.610 0.978

0.948

−0.890

−0.955

1. Environmental modifications, degradation and human health risks

1.000

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4.  Changes in nighttime lights during COVID-19 lockdown over Delhi, India

• The correlation between EPC and NTL was found to be a relatively high negative value (−0.727), showing an inverse relationship between the two. This might be because of the dataset considered for EPC which does not provide any insights on where the electricity consumed is from luminous or indoor appliances. • Another inference drawn from the correlation results obtained is that the NTL and confirmed COVID cases shows a negative correlation of −0.605 and between deaths due to COVID-19 and NTL is −0.406. The more there is spread of COVID-19, the less outdoor lights are captured; implying that days when more COVID cases were reported, people tend to move lesser on those nights, leading to lower outdoor NTL.

4.4.4  Results for regression-based approach for prediction of EPC The processed EPC data along with other datasets during COVID-19 lockdown period is then used to fit a model equation for predicting future EPC after COVID-19 lockdown months. The Eq. 4.1 is obtained as the model equation for EPC. EPCmodelled = 232 − 0.268 NTL − 0.000992GP 2 (4.1)

where,

EPCmodelled = percentage deviation of EPC from baseline NTL = percentage deviation of NTL from baseline GP = percentage deviation of mobility in Grocery and Pharmacy, from baseline The modelled Eq. (4.1) is in line with (Sahoo et al., 2020) which also predicted that EPC can be modelled using NTL. The model Eq. (4.1) obtained has a mean absolute error, MAE = 11.536 with goodness of fit (coefficient of determination) R2 = 0.988. In Eureqa, the complexity of the model equation indicates the complexity of the terms used to build equation while fit is a standardized metric which decreases as the accuracy of the model equation increases. We see that the obtained model Eq. (4.1) of EPC not only depends on NTL but also has a factor of GP2 which is the mobility in sectors of grocery and pharmacy. This proves our hypothesis that the EPC during COVID19 lockdown depends on other factors prevailing during COVID-19 lockdown, along with NTL. This gives us an insight that the future predictions on EPC can be done by considering the factor of mobility in commercial areas like pharmacies, etc., and not just on NTL. This can be helpful for policy makers responsible for optimal distribution of electric power within a state. Eureqa generates all possible model equations for EPC. Equations containing more independent variables (such as mobility in residential areas, transit stations etc. and COVID cases) were also generated. However, the weightage assigned to them was very low. Therefore Eq. (4.2) was selected which had the two most important variables (NTL and mobility in grocery and parks). This made the model more understandable and fit-for-purpose. This equation also proves our hypothesis that the EPC during lockdown has an impact of other ancillary variables like mobility in various sectors along with NTL. For further validation of our hypothesis, accuracy assessment can be done for the predicted results obtained through modelled equation to the actual data, as discussed in recommendations.

4.5  Conclusions and recommendations This study explores the changes in NTL during COVID-19 lockdown and studies its impact on the electricity sector in Delhi. This can be concluded from our study, that the correlation between EPC and NTL has been impacted during lockdown months from March 25, 2020 to May 31, 2020. As we compare the correlation of NTL and EPC for the same period in 2019, there seems to be a high positive correlation for NTL < 20 nW.cm−2.sr−1 for low lit residential areas and high negative correlation for NTL > 20 nW.cm−2.sr−1 for high lit commercial areas. This correlation ceases to exist for the lockdown months of 2020 where there is weak correlation between NTL and EPC for both the ranges. This decrease in dependence of EPC on NTL can be attributed to the impact of other variables like mobility and COVID cases on the EPC during lockdown months. For further insights on this, the correlation between multidomain dataset is studied. Various approaches are adopted to harmonize the multidomain datasets, temporally, spatially, and quantitatively like expressing dataset as percentage change in deviation from baselines, taking median of NTL to bring to state-level, considering moving averaged data with a window size of 12 days (which is in sync with the incubation period of COVID-19). The multidomain correlation results give insights on the relationship of each variable with other variables during the lockdown period. A high positive correlation between EPC and mobility in retail and recreation sectors, grocery and pharmacy sectors, etc., is seen which 1. Environmental modifications, degradation and human health risks

References 47

corresponds to the dependence of EPC on these variables. The negative high correlation is also seen between NTL and EPC. But this can be due to the nonavailability of breakup of electricity sector data into various sectors of consumption. Finally, a model equation is obtained using symbolical regression-based approach where the modelled EPC is expressed as a function of NTL and also depends on the second order of percentage change in deviation from baseline in mobility for grocery and pharmacy stores. This model equation can be used to conclude that the EPC during COVID-19 lockdown is not just dependent on NTL but is also impacted by other factors like mobility. In the future work the following points are recommended for getting improved results: • The EPC data from POSOCO being considered in our analysis, does not give a breakup of electric power consumed in various sectors like residential, commercial, public places etc. For better analysis and correlation results, a dataset, which categorizes the consumption based on different sectors, can be used. • While studying the correlation between multidomain dataset, the NTL data has been considered as a single value brought to the state level by taking median of lights from percentage change in deviation from baselines. If the radiance values of NTL are categorized in various ranges as discussed in Section 4.2, better insights on the correlation between different variables can be obtained. • The model equation for prediction of EPC for post COVID scenario can be obtained considering more complex terms for accurate predictions. Because of limited time for the study, a simple equation was considered which proved our hypothesis. Also, accuracy assessment for the predicted results should be included for further validation of the modelled equation. • The mobility dataset made available from Google gives a lumped categorical value of temporal deviation for each Indian state. The spatially distributed geometries which are used to construct these categorical mobility deviations, if provided, can help expand this study to obtain more “spatial” results.

Acknowledgements This study is a part of P.G. Diploma dissertation carried out by the first author. Thanks to the Director, Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO), Dehradun, India for his support and encouragement. Also, thanks are due to the Faculty of Geoinformation Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands. Sincere thanks to NASA LAADS DAAC, POSOCO, covid19india.org, and Google for providing the Black Marble NTL, EPC, COVID-19 cases and mobility datasets, respectively.

References Centres for Disease Control and Prevention, 2020. Symptoms of Coronavirus. CDC. https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html. (Accessed 22 August 2020). COVID-19 India API, 2020. covid19india.org. https://api.covid19india.org/. (Accessed 25 July 2020). COVID19 Statewise Status, 2020. MyGov.in. https://www.mygov.in/corona-data/covid19-statewise-status. (Accessed 31 August 2020). Daily Reports - National Load Dispatch Centre, POSOCO, 2020. POSOCO Limited. https://posoco.in/reports/daily-reports/. (Accessed 21 July 2020). Donalisio, M.R., Souza, C.E., Angerami, R.N., Samy, A.M., 2020. Mapping Brazilian spotted fever: Linking etiological agent, vectors, and hosts. Acta Tropica 207, 105496. https://doi.org/10.1016/j.actatropica.2020.105496. Elvidge, C., Ziskin, D., Baugh, K., Tuttle, B., Ghosh, T., Pack, D., et al., 2009. A fifteen-year record of global natural gas flaring derived from satellite data. Energies 2 (3), 595–622. Falchetta, G., Kasamba, C., Parkinson, S.C., 2020. Monitoring hydropower reliability in Malawi with satellite data and machine learning. Environ. Res. Lett., 15 (1), 014011. https://doi.org/10.1088/1748-9326/ab6562. Falchetta, G., Noussan, M., 2019. Interannual variation in night-time light radiance predicts changes in national electricity consumption conditional on income-level and region. Energies 12 (3), 456. https://doi.org/10.3390/en12030456. Falchetta, G., Pachauri, S., Parkinson, S., Byers, E., 2019. A high-resolution gridded dataset to assess electrification in sub-Saharan Africa. Sci. Data 6 (1), 110. https://doi.org/10.1038/s41597-019-0122-6. Google COVID-19 Community Mobility Reports, 2020. Google LLC. https://www.google.com/COVID-1919/mobility/. (Accessed 16 July 2020). Lai, S., Farnham, A., Ruktanonchai, N.W., Tatem, A.J., 2019. Measuring mobility, disease connectivity and individual risk: A review of using mobile phone data and mHealth for travel medicine. J. Travel Med., 26 (3), taz019. https://doi.org/10.1093/jtm/taz019. Liu, Q., Sha, D., Liu, W., Houser, P., Zhang, L., Hou, R., Lan, H., Flynn, C., Lu, M., Hu, T., Yang, C., 2020. Spatiotemporal Patterns of COVID19 Impact on Human Activities and Environment in China Using Nighttime Light and Air Quality Data. ArXiv:2005.02808 [Physics]. http://arxiv.org/abs/2005.02808.

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4.  Changes in nighttime lights during COVID-19 lockdown over Delhi, India

NASA LAADS DAAC (VNP46A1-5000), 2019. Black Marble Product Suite Version A1. NASA. https://ladsweb.modaps.eosdis.nasa.gov/ search/order/2/VNP46A1–5000. (Accessed 4 July 2020). Román, M.O., Stokes, E.C., Shrestha, R., Wang, Z., Schultz, L., Carlo, E.A.S., Sun, Q., Bell, J., Molthan, A., Kalb, V., Ji, C., Seto, K.C., McClain, S.N., Enenkel, M, 2019. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLOS ONE 14 (6), e0218883. https://doi.org/10.1371/journal.pone.0218883. Román, M.O., Wang, Z., Shrestha, R., Yao, T., Kalb, V., 2019. Black Marble User Guide Version 1.0. NASA, Washington, DC, USA. Román, M.O., Wang, Z., Sun, Q., Kalb, V., Miller, S.D., Molthan, A., Schultz, L., Bell, J., Stokes, E.C., Pandey, B., Seto, K.C., Hall, D., Oda, T., Wolfe, R.E., Lin, G., Golpayegani, N., Devadiga, S., Davidson, C., Sarkar, S., Masuoka, E.J., 2018. NASA’s Black Marble nighttime lights product suite. Remote Sens. Environ., 210, 113–143. https://doi.org/10.1016/j.rse.2018.03.017. Ruan, G., Wu, D., Zheng, X., Sivaranjani, S., Zhong, H., Kang, C., Dahleh, M.A., Xie, L., 2020. A cross-domain approach to analyzing the short-run impact of COVID-19 on the U.S. electricity sector. SSRN Electron. J. https://doi.org/10.2139/ssrn.3631498. Sahoo, S., Gupta, P.K., Srivastav, S.K., 2020. Comparative analysis between VIIRS-DNB and DMSP-OLS night-time light data to estimate electric power consumption in Uttar Pradesh, India. Int. J. Remote Sens., 41 (7), 2565–2580. doi:10.1080/01431161.2019.1693077. Schmidt, M., Lipson, H., 2009. Distilling free-form natural laws from experimental data. Science 324 (5923), 81–85. https://doi.org/10.1126/ science.1165893 . Small, C., MacDonald, A.J., Sousa, D., 2020. Spatial network connectivity of population and development in the USA; Implications for disease transmission. ArXiv:2004.14237 [Physics]. http://arxiv.org/abs/2004.14237. Tizzoni, M., Bajardi, P., Decuyper, A., Kon Kam King, G., Schneider, C.M., Blondel, V., Smoreda, Z., González, M.C., Colizza, V., 2014. On the use of human mobility proxies for modeling epidemics. PLoS Comput. Biol., 10 (7), e1003716. https://doi.org/10.1371/journal. pcbi.1003716. Zhao, Zhou, Li, Cao, He, Yu, Li, Elvidge, Cheng, Zhou, 2019. Applications of satellite remote sensing of nighttime light observations: advances, challenges, and perspectives. Remote Sens., 11 (17), 1971. https://doi.org/10.3390/rs11171971.

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C H A P T E R

5 Socio-environmental factors affecting mental health of people during COVID-19 in coastal urban areas of Bangladesh Rozina Aktera, Mukta Aktera, Md. Tanvir Hossainb, Md. Nasif Ahsana a

Economics Discipline, Social Science School, Khulna University 9208, Bangladesh Sociology Discipline, Social Science School, Khulna University 9208, Bangladesh

b

5.1 Introduction The COVID-19 has become the paramount global challenge in recent times because of its devastating footprint upon China, the USA, Italy, the UK, Brazil, and more than 200 other countries and territories (Fitzgerald and Wong, 2020). People across the world are getting accustomed to the new emergent conditions, such as “lockdown,” “quarantine,” and “isolation” over the last few months. The episode of COVID-19 was out broken first from Wuhan, China on December 31, 2019 (Lee, 2020). Symptoms including fever, sore throat, dry cough, tiredness, and suffocation are noticed generally within first two weeks of being infected by COVID-19 (Ahorsu et  al., 2020). As of August, just over 21 million people are found COVID-19 positive in 216 countries and territories, and just over 0.76 million succumbed to death globally (WHO, 2020a). In January 2020, after traveling Wuhan, an old lady died in New York and following this incident the government took necessary initiatives to curb the human-to-human transmission. Nearly, 4.6 million people are infected and another 154 thousand are prayed for rest in peace until first week of August 2020 in the USA only (Velásquez and Lara, 2020; WHO, 2020c). At the end of March 2020, official database of Italy portrayed a brutal face of COVID-19 with nearly 106 thousand positive cases, of which 15.1 thousand lost their lives (Chintalapudi et  al., 2020). In India, near about 3.5 million people are infected by COVID-19 with 62.5 thousand deaths (WHO, 2020b). World research fraternity suspects that India can be the next noxious destination of COVID19 with high transmission rate. In the first week of August, there were around 0.3 million cases are confirmed of COVID-19, killing 3.3 thousand people death cases in Bangladesh (WHO, 2020a). Medical researchers are attempting to discover effective vaccine (Dong et  al., 2020). National and international health organizations keep updating relevant information, recommendation, and opinion on COVID-19. However, researchers and health organizations significantly ignored the psychological facet of COVID-19. Experts opine that up to 70% COVID-19 positive cases require both physical and mental health treatment (Lee, 2020). The COVID-19 pandemic hampers mental condition of the people at risk by exacerbating the level of anxiety, depression, stress, contamination concern, and finally suicidal tendency (Lee, 2020; Gavin et al., 2020; Kahambing and Edilo, 2020; Pompeo-Fargnoli and Fargnoli, 2020; Taylor et al., 2020; Tanoue et al., 2020; Islam et al., 2020a). Mental health becomes a burning issue in the event of recent outburst of pandemic COVID-19 having great impact on physical and mental health of people at risk across the globe. Anxiety about COVID-19 is the fiend of having excessive psychological stress. The prevalence of post-traumatic stress disorder has boosted from 4 to 41% and severe depression rose by 7% globally at the time of this crisis (Torales et al., 2020). At present, people became familiar with new attributes, such as- work from home, education from home, less physical interaction with family, friends, and colleagues. These new attributes trigger new sources of mental unrests (Pompeo-Fargnoli and Fargnoli, 2020; WHO, Environmental Resilience and Transformation in times of COVID-19. DOI: 10.1016/C2020-0-02703-9

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5.  Socio-environmental factors affecting mental health of people during COVID-19 in coastal urban areas of Bangladesh

2020a; Tanoue et  al., 2020). In Canada, 47% healthcare service providers were reported to require mental support (WHO, 2020b). Study results of Brennan et al. (2020) revealed that older people are going through psychosis problem in Ireland due to dramatically change of life. Since the outbreak of COVID-19 insufficient health care facilities, lack of opportunity to see loved once for the last time, and funeral rituals influence people’s psychological health (Brennan et al., 2020). A study on COVID-19 patients in China found that admitted patients required both physical and mental health treatment even after discharging from hospital (Hu, et  al., 2020). Under these circumstances, depression, anxiety, and insomnia prevailed for 45.9%, 38.8%, and 54.1% cases, thus, implying mental health problem appears to last for a considerable time period (Hu et  al., 2020). Furthermore, rate of depression in Ethiopia has increased three-fold compared to the pre COVID-19 situation where medical staff and officials are experiencing an abnormal level of depression and stress, they have to deal with a surge of positive cases and deaths on daily basis (WHO, 2020c). Yeasmin et al. (2020) found in Bangladesh, children were significantly affected by depression, anxiety and insomnia as a result of parent’s unstable mental condition for COVID-19 situation. Another study on university students in Bangladesh revealed that a notable percentage of students reported to suffer from moderate to severe depression and anxiety symptoms during the pandemic (Islam et al., 2020b). Diverse factors are reported to influence the mental health of the people during COVID-19. For example, a study on different cohort in Bangladesh denoted that socioeconomic status, along with exposure to mass media, is culpable for escalating degree of anxiety during COVID-19 in Bangladesh (Hossain et al., 2020). Furthermore, different socioenvironmental factors are also likely to affect the mental health of the people in Bangladesh though little or no evidence exists in this regard. Socioenvironmental factors are highly associated with health behavior by forming social ethos and positive health synergy while sapping negative health behavior (Mama et al, 2016; Bourque et al., 2012). Having unequal socioeconomic opportunities may foster anxiety, depression, stress, sleeping disorder, and social disunion (Li and Liu, 2018; Lee, 2020; Gavin et al., 2020; Taylor et al., 2020). During COVID-19 both social and physical environment are taking a precarious form in the society (Tunstall et al., 2014). At present 55% global population live in urban areas and by 2050 almost 68% world population are going to live in the aforesaid areas (WHO, 2020b). Italy, one of the most urbanized country with seemingly sufficient medical facilities, faced a rapid COVID-19 transmission rate even before announcing lockdown (Sangiorgio and Parisi, 2020). While in Bangladesh, only 41 laboratories situated in urban areas to test COVID-19 samples and in some cases, samples are tested after the demise of COVID-19 patients (Shammi et al., 2020). In Bangladesh, the rate of COVID-19 infection is higher among urban residents than those of rural areas (Sakamoto et al., 2020). Considering this scenario, it is necessary to pay special attention for urban areas in Bangladesh to curb the COVID-19 transmission rate (Sangiorgio and Parisi, 2020). Since March 18, 2020, the Government of Bangladesh decided to keep all educational institutions closed across the country to minimize the transmission rate of COVID-19 (Yeasmin et al., 2020). As an initiative to abate the spread of pandemic, government followed global trend and declared lockdown in a disguise of general holiday from March 26, 2020 to May 31, 2020 (Bodrud-Doza et  al., 2020). Institute of Epidemiology, Disease Control, and Research (IEDCR) revealed first three positive cases in the first week of March 2020 (Islam et  al., 2020c). Since then, the COVID-19 positive cases are growing rapidly in the urban areas (Shammi et al., 2020), although urban people have been following the preventive measures, such as wearing mask, cleaning hands with sanitizer and soap, and avoiding gathering and physical contact with nearest and dearest ones. The prolong confinement, however, is alarmingly damaging the mental health of urban people by encountering fear of- being infected, losing loved once, misleading information, inadequate medical equipment and treatment, prolonged isolation, job loss, supply shortage of required food, movement restriction, and social distance. For example, on March 25, 2020, a middle-aged Bangladeshi citizen (a suspected COVID-19 patient) committed suicide because of changing social behavior toward COVID-19 patients, though his COVID-19 report was negative (Mamun and Griffiths, 2020). Researchers across the world are busy to discuss about infection control, projection on future spread rate, and effective vaccine with a little emphasis on mental well-being of people. A good number of studies on COVID-19 pandemic attempted to address mental health to different groups of people, such as medical staffs and officials, front- line workers, older people, students, children, COVID-19 patients (Brennan et al., 2020; Yeasmin et al., 2020; Gavin et al., 2020; WHO, 2020b; Hu et al., 2020; Islam et  al., 2020a; Hossain et  al., 2020). Furthermore, the role of socioenvironmental factors on mental health during COVID-19 appears to be an issue that is yet to be addressed, especially in urban areas of developing countries. Policymakers, health officials, and medical practitioners need to understand the nature and level of psychological impact of socioenvironmental factor to formulate new strategies and policies to adjust with the “new normal.” In this backdrop, the present study aims to present a snapshot regarding the role of socioenvironmental factors on mental health of people during COVID-19 in the costal urban areas of Bangladesh by analyzing two research questions: a) what is the role of socioenvironmental factors in regulating the mental health (anxiety and stress) of people in the costal urban area; and b) how COVID-19 affects the mental health of urban costal people in Bangladesh. 1. Environmental modifications, degradation and human health risks



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5.2 Method 5.2.1  Participants and data collection procedures Data were collected from the coastal urban areas of Bangladesh deploying a web-based questionnaire (google-form). The participants were selected based on specific criteria, namely- (i) a citizen of Bangladesh, (ii) more than 15 years of age, (iii) living in urban areas of southwestern coastal region of the country, and (iv) having an email and/or social media account. Based on the aforementioned criteria, the questionnaire was shared with the participants using social media, e.g., Facebook, messenger, and email to collect information regarding their health status and perception about COVID-19. During a period of 14 days (01–14 August, 2020), we received responses from 128 respondents of whom we could finally include 115 in our sample. The rest of the responded were discarded due to nonresponse to a good number of questions.

5.2.2 Measures Along with COVID-19 related questions, respondents were asked about their demographic features and socioenvironmental characteristics. Data collection began when the pandemic had spread out throughout the country and it seemed to go toward the saturation point in terms of number of affected people. In order to assess the mental health condition during the pandemic, respondents were asked about their prior and post health condition. They were also asked whether they are satisfied with the medical treatment or not through a five-point Likert scale (Highly dissatisfied = 1 to Highly satisfied = 5). Coronavirus Anxiety Scale (CAS) and Composite COVID-19 Stress Index (CCSI) were used for finding out the outcome of COVID-19 on mental health. Some specific factors had been selected through these scales to measure the level of stress and anxiety among the respondents. A complete list of variables used in this study is presented in Appendix 1. 5.2.2.1  Personal attributes and socioeconomic status Demographic and socioeconomic factors influence the mental health and physical reactions toward any situation (Taylor et al., 2020). That is why the influence of personal attributes of the respondents, like age (in year), sex, literacy level, marital status, religion, and family type, had been assessed in this study. In addition, the role of employment status, income, expenditure, and household ownership of the respondents had also been analyzed. Relevant descriptive statistics were also produced for presenting the socioeconomic and personal attributes of the respondents. 5.2.2.2  Socioenvironmental factors Socioenvironment is the immediate physical and cultural structure within which the people live. In this study, we consider all the surrounding elements, way of communication, physical utilities, and thoughts and beliefs among the dwellers in different localities are the main factors of social-environment. In this study, we measured socioenvironmental factors by considering living environment (1 = less noisy to 5 = very noisy), security status (1 = less secured to 5 = highly secured), rate of criminal activity (1 = very low to 5 = very high), source of drinking water (own pump; filter or bottle water) of the concerned locality (see Appendix 1 for detail). 5.2.2.3  Health status and care-seeking behavior In this study, we used a group of items for examining the physical health status of the respondents and their family members. Frequency of illness (in number), type of illness (e.g., fever, cold, cough, headache, sneeze, asthma, rheumatics or distaste), days suffered (i.e., duration) for COVID-19 illness, number of physically challenged household members, number of mentally challenged household members and so on were used to measure the health status of the respondents (see Appendix 1 for detail). 5.2.2.4  Composite COVID-19 stress index Composite COVID-19 stress index (CCSI) is a customized multifactorial parameter for assessing mental distress among people independent of physical status during any pandemic. It is formulated in such a way that it can be readily used in any pandemic situation that may arise in the future. In CCSI model, four factors have been identified related to the stress of COVID-19, including agitation, scarcity (of necessary commodities), trauma, and infodemic. A five-point Likert scale was used in this case (where, 1 = strongly disagree…5 = strongly agree) to reveal out the impact. A total of 39 variables for CCSI were adapted from Taylor et al. (2020) and customized in line with

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5.  Socio-environmental factors affecting mental health of people during COVID-19 in coastal urban areas of Bangladesh

Bangladesh context. These variables were broadly from five dimensions namely- fear of danger, socioeconomic vulnerability, traumatic stress, xenophobia and compulsive checking, and reassurance seeking. Final score value of CCSI is obtained by summing up concern scores from all variables. This index value ranges from 44 to 105. 5.2.2.5  Coronavirus anxiety scale We parallelly applied Coronavirus anxiety scale (CAS) along with CCSI in the study where CAS measured the anxiety level of the respondents. It is a mental screener that determines the mental dysfunctionality associated with COVID-19. Following the study by Lee (2020), we selected concern variables for CAS from four dimensions, namelycognitive (e.g., nightmare, worry); behavioral (e.g., compulsive behavior); emotional (e.g., fear, anger, anxiety) and physiological (e.g., sleep disturbance) dimensions of COVID-19. In order to measure the level of anxiety among the respondents regarding COVID-19, five questions were asked to the respondents using a five-point Likert scale to assess the effect of COVID-19 on participants’ mental health (see Appendix 1 for detail). Concern values were summed up to obtain score for this CAS where the sore ranges from 5 to 19.

5.2.4  Analytical tools In this study we applied exploratory factor analysis (EFA), which is a multivariate statistical tool used for extracting the principal dimensions representing the physiological behavior of the respondents. For testing the normality of the data, maximum likelihood approach was used to minimize errors in the data. Cronbach’s alpha was also estimated to assess the internal consistency. Sample size is important in factor analysis model. Different opinions were found in this respect where the size varies significantly. However, the minimum sample requirement for performing EFA is between 100 and 1000 or more. This study satisfied this requirement as it dealt with a sample over 100. At the same time, sample to variable ratio and Kaiser–Meyer–Olkin (KMO), measure of sampling adequacy/Bartlett’s test of sphericity were also used to assess the suitability of the data for EFA. To simplify the factor structure of a group of items, high item loadings on one factor and smaller item loadings on the other factors were imposed through using Principal Component Analysis (PCA) approach. Cumulative percentage of variance and eigenvalue were assessed in this study where cumulative percentage of variance was 50% or above with a minimum of three factors/variables were considered for factor analysis. Moreover, Cattell’s scree plot test and parallel analysis were also conducted in this study. In case the actual Eigenvalue surpassed random ordered Eigenvalues, the factor was retained from the group of factors. Following the results of EFA, we applied an ordinary least square (OLS) regression model to evaluate the socioenvironmental factors affecting the degree of coronavirus triggered anxiety (CAS). In this regard, we considered the dependent variable as the values of CAS while a bunch of explanatory variables including the predicted principal components were considered.

5.3 Results 5.3.1  Socioeconomic characteristics of the respondents The descriptive statistics denote that most of the respondents (about 40%) were from Khulna region and the average household size is around 5 members in the study area. The study-result presents that about 90% of the respondents were between the age group 15–35 and among these respondents- 60% were male. Seventy one percent respondents reported to be unmarried while their literacy level (with average schooling years of 22 years) denotes that none of them were illiterate. Result also demonstrates that 80% respondents were Muslims and the rest 20% were followers of Hinduism. Male-female ratio was found to be approximately 1.50:1 in the study area. Nearly 33% respondents reported to have physically challenged members in their households and of this group, 13% of them suffered mental problems. Though around 33% of the respondents were paid workers (employed), nearly 60% of them were students and about 5% were unemployed. The study shows that the housing condition of the sampled respondents was good enough since 71% households lived in a Paka houses (i.e., brick-built) and about 20% in a Semi-Paka (semi brickbuilt) houses. Moreover, 66% of the respondents owned a house and only 46% of them lived in a rented house. For their drinking water, 40 respondents availed own pump/tube-wells; about 17% used boiled or filtered water; 14% used supply line, and about 8% used bottle water. Composition of household-structure suggest that nearly 66% respondents were from nuclear family whereas nearly 20% was from joint family while only about 14% respondents

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were from Akannoborty family (large joint family). Number of living rooms in the household was mostly found in between two and seven rooms (86%) with maximum of eight rooms. On the other hand, only about 4% households had less than two rooms and about 8% had more than seven rooms in their house. Average monthly income of the respondents was found to be approximately BDT 28,500 (±36,739) while average monthly expenditure was about BDT 23,000 (±25,442). The income and expenditure distribution pattern denote that there existed a wide range of disparity among the respondents since the estimated standard deviation was so high in case of both income and expenditure. This study also seeks for the socioenvironmental status of the localities of the respondents implying the environment of the locality was moderately noisy and highly secured to live in. Similarly, on an average frequency of criminal activity was also moderate around these localities.

5.3.2  Exploratory factor analysis The latent components from the 39 variables with five-point Likert-scale items measuring the coronavirus-induced stress were explored by the exploratory factor analysis (EFA) using the maximum likelihood extraction method. The Kaiser-Meyer-Olkin (KMO) and Bartlett’s test of sphericity analyses in the preliminary Principal Component Analysis (PCA) signified the sampling adequacy as the KMO value was 0.763, which is higher than the benchmark value of 0.600 (Hair et al., 2014; Tabachnik and Fidell, 2013), while the Bartlett’s test of sphericity was also found significant (χ2 [210] = 1484.933, p < 0.001). To determine the numbers of latent components, the decisions was guided by threerules guideline, (i) Kaiser’s criterion, (ii) Cattell’s scree plot test, and (iii) Horn’s parallel analysis (Pallant, 2011; Pallant and Bailey, 2005). The Kaiser’s criterion based on eigenvalues were inconclusive as it suggests 10 factors with an eigenvalue of 1 and above. In contrast, the scree plot test endorses a four-factor solution by accepting the “higher scree” and ignoring the “lower scree” (Cattell, 1966). The four-factor solution and its utility was assessed further by comparing the eigenvalues from the PCA with the eigenvalues generated from the same size of random data set, where the factors with the eigenvalues exceeding the values from randomized data were retained for analysis (Horn, 1965; Watkins, 2000). The parallel analysis also recommends a four-factor solution. Moreover, (i) pattern coefficients >0.50 on one item for practical significance (Hair et al., 2014), (ii) ≥three items with salient pattern coefficients (Jones et al., 2017) and (iii) an internal consistency of ≥0.70 was considered for a meaningful and consistent factor structures (DeVellis, 2003). Table 5.1 presents results of PCA where 21 variables were obtained. Based on the above-mentioned criteria, a four-factor solution was retained from the EFA, which was explained by 21 variables (out of 39 variables). Each of the factor was addressed by a cluster of variables. The explored fourfactors correspond to (i) agitation, explaining 26.6% of the variance with a Cronbach’s α of 0.89 (i.e., internal consistency), entailed the items associated with the fear of contamination in social interaction, (ii) scarcity, with an internal consistency of 0.90 explaining 14% variance, referred to the items related to the fear of shortage of basic commodities and amenities during the pandemic, (iii) trauma, with a total variance of 11% and internal consistency of 0.87, consisted of mental health issues triggered by the pandemic, and (iv) infodemic, referring to the over-exposure or access to information regarding the health situation, explained around 8% variance with an internal consistency of 0.82. The four-factor measurement of coronavirus stress, explaining 59% of the total variance with an overall reliability of 87%, indicates that the measurement could possibly be applicable for other similar studies. Therefore, results of EFA suggest- the stress of COVID-19 for respondents was governed by the aforesaid four factors- agitation, scarcity, trauma, and infodemic. Each of these factors was addressed by a cluster of variables. For example, agitation is represented by seven variables which reflect mostly the notion of socioeconomic and contaminating attributes posed by COVID-19; scarcity is represented by five variables mainly indicating xenophobic attribute of the respondents; trauma is represented by six variables exhibiting stress from xenophobic situation; and finally infodemic is represented by three variables showing the characteristics of checking with updates of COVID-19.

5.3.3  Socioenvironmental factors affecting COVID-19 At this point, we inspect the role of socioenvironmental factors on COVID-19 by applying an ordinary least squared (OLS) regression model presented in Table 5.2. While analyzing this OLS, we tested for multicollinearity and heteroscadisticity. This model encountered only heteroscadisticity problem and thus, we apply a weighted (ordinary) least squared (WLS) regression model by applying an analytical weight. For this analytical weight, we used the fourth power to ‘number of ailments’. This time obtained results of the weighted regression model did not encounter neither heteroscadisticity problem (χ2(1) = 1.58 (p < 0.208) nor multicollinearity problem (VIF = 2.18) For this regression model presented in Table 5.2, we considered scores of Coronavirus Anxiety Scale (CAS) as dependent variable 1. Environmental modifications, degradation and human health risks

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5.  Socio-environmental factors affecting mental health of people during COVID-19 in coastal urban areas of Bangladesh

TABLE 5.1  Exploratory factor analysis (N = 115). Factor

Communalities (extracted)

Agitation

I worry about taking change in cash transactions

0.739

0.850

CSS_20

I worry that my postal mail has been contaminated by mail handlers

0.658

0.790

CSS_17

I worry that people around me will infect me with the virus

0.565

0.739

CSS_19

I worry that I might catch the virus from handling money or using ATM

0.589

0.709

CSS_16

I worry that if someone coughed or sneezed near me, I would catch the virus

0.413

0.630

CSS_15

I worry that if I touched something in public space (e.g., handrail, door handle), I would catch the virus

0.408

0.624

CSS_14

If I was in an elevator with a group of people from other areas, I’d be worried that they are infected with the virus

0.398

0.619

CSS_9

I worry about grocery stores running out of cleaning or disinfectant supplies

0.741

0.844

CSS_11

I worry about departmental stores running out of drinking water

0.653

0.798

CSS_8

I worry that grocery and departmental stores will close down

0.685

0.797

CSS_10

I worry about drug-stores running out of cold or flu remedies

0.653

0.791

CSS_7

I worry about grocery stores running out of food-items

0.500

0.680

CSS_23

I had nightmare because I worried about the virus

0.748

0.797

CSS_25

Reminders of the virus causes me to have physical reactions, such as- sweating or a pounding heart

0.604

0.756

CSS_24

I think about the virus when I do not mean to

0.626

0.732

CSS_21

I have trouble concentrating because I kept thinking about the virus

0.565

0.702

CSS_26

I have nightmares about the uncertain future

0.502

0.647

CSS_22

Disturbing mental images about the virus pop into my mind against my will

0.422

0.551

CSS_28

I am always updated about COVID-19 infection status within the country

0.833

0.906

CSS_29

I am always updated about COVID-19 infection status outside the country

0.787

0.881

CSS_33

I have always received COVID-19 related necessary information

0.313

0.550

Items

Description

CSS_18

Scarcity

Trauma

Infodemic

Variance explained (percent)

26.586

13.530

11.213

7.722

Cronbach’s α

0.887

0.895

0.874

0.816

1. Environmental modifications, degradation and human health risks



55

5.3 Results

TABLE 5.2  Regression results on factors affection coronavirus-related anxiety. Variables

Description

Coefficients (standard errors)

Agitation

Predicted first component

0.710** (0.280)

Scarcity

Predicted second component

0.626* (0.356)

Trauma

Predicted third component

0.297 (0.424)

Infomdemic

Predicted fourth component

−2.313*** (0.448)

Age

Years

0.236*** (0.0531)

Education

Year of schooling

−0.460*** (0.0832)

Living condition

Reference group: soil and straw shed

Source of water

Criminal activity

Number of ailments

Soil and tin shed

1.078 (1.389)

Soil, fence, and straw shed

−0.0253 (0.977)

Semi-Paka (semi brick-built)

−3.359*** (1.096)

Paka (brick-built)

−1.147 (1.910)

Reference group: own pump Supply line

−3.411*** (0.854)

Boiled/filtered water

−1.647** (0.712)

Bottled water

−3.594*** (0.795)

Others

−4.004 (2.473)

Reference group: very low Neutral

−1.641*** (0.593)

High

0.0977 (0.913)

Very high

−0.319 (0.848)

Reported number of illness from a household

1.172*** (0.182)

Constant

6.733** (2.945)

Observations

88

R-squared

0.928

*Significant at 10% level. **Significant at 5% level. ***Significant at 1% level.

while a number of relevant socioenvironmental factors were represented as explanatory variables. We also considered predicted values of the four principal components extracted via EFA as explanatory variables in this WLS model. WLS regression results presented in Table 5.2 denote how socioenvironmental features affected the CAS. First (i.e., agitation) and second (i.e., scarcity) components of EFA exhibited a significant positive relationship with Coronavirus Anxiety Scale (CAS) implying as concerned degree of agitation and scarcity increased, COVID-19 related anxiety also increased. While the fourth component of EFA (infodemic) implied, access to information regarding the health situation lessened significantly the anxiety level during coronavirus pandemic. Other results suggest that elderly people significantly suffered from the coronavirus anxiety while the literate respondents were significantly less affected by anxiety level during coronavirus pandemic. Results on living condition in terms of structural settlement suggested that respondents living in semi brick-built houses significantly encountered lower degree of anxiety while respondents living in brick-built and relatively weakly built (made of soil, fence, and straw) houses also exhibited an inverse relationship with degree of corona-related anxiety though the results were not statistically significant. Sources of drinking water also exhibited significant inverse relationship with CAS denoting that sources 1. Environmental modifications, degradation and human health risks

56

5.  Socio-environmental factors affecting mental health of people during COVID-19 in coastal urban areas of Bangladesh

of drinking water such as supply line, boil filter, and bottle water abridged the degree of coronavirus related anxiety. A statistically significant inverse relationship was obtained for respondents’ neutral notion on relationship between Criminal activity in respondents’ surrounding areas and corona-related anxiety. The mean coefficient for ailments was found significantly different than zero with a positive sign implying that number of illness significantly escalated the degree of coronavirus anxiety level significantly. Though this WLS model could consider only 88 respondents, the overall goodness of fit of this model was found slightly over 90% indicating this model explained just over 90% variation in the dependent variable.

5.4 Conclusion The COVID-19 pandemic has introduced a complex hazard for the people across the globe. The main objective of this study was to present an overall scenario on role of socioenvironmental factors on mental health of people during COVID-19 in the costal urban areas of Bangladesh. Major findings of this study suggest that during COVID-19, socioenvironmental factors have affected mental health of the people in different way with a different category. in addition, we have applied customized Composite COVID-19 Stress Index (CCSI) and Coronavirus Anxiety Scale (CAS) to realize the study objective. As analytical tool, exploratory factor analysis (EFA) and regression model were used. Variables used in CCSI model were broadly from five dimensions namely- fear of danger, socioeconomic vulnerability, traumatic stress, xenophobia and compulsive checking, and reassurance seeking; which all are consistent with the study by Taylor et  al. (2020). On the other hand, variables of CAS were broadly from four dimensions such as- cognitive, behavioral, emotional, and physiological. Applying EFA with CCSI, four factors namely- agitation, scarcity, trauma, and infodemic have been obtained and these four factors are explained by 21 variables from a set of 39 variables (see Tables 5.1 and 5.2). Empirical results from EFA suggest that respondents were highly worried about being infected through social interaction because COVID-19 is a transmissible disease. In addition, the anxiety level seemed to hike the fear of uncertainty of future availability of essential commodities including food, water, and medicine. This finding is consistent with the study of Islam et al. (2020a). Thinking all the time about COVID-19 affects mental health by generating sleeping disorder as well as subconsciously setting up of a xenophobic scenario in mind. Therefore, respondents have been going through traumatic situation because of this current pandemic. Furthermore, using internet for COVID-19 treatments, information, and checking with body signs are some important issues that exacerbated societal instability. Findings of this study also denote that the respondents seemed to be overexposed while accessing to required information for their health situation. Results from liner regression model (i.e., a Weighted Least Squared regression model) examine that fear of contamination in social interaction and shortage of basic amenities play a crucial role for boosting the degree of Coronavirus Anxiety Scale (CAS) during COVID-19 pandemic. In line with the result from the study by Fitzgerald and Wong (2020), empirical result of this study also suggests that fear of inadequate supply of food and medicine enhances the degree of anxiety. In contrast- infodemic, over-exposure or access to information regarding health implies an indirect relation with the degree of anxiety. Under this pandemic situation, people usually set an image of virus on mind and hence, they cannot pass a healthy day without thinking about the impact and aftermath uncertainty of their lives. At this point, socioenvironmental factors such as- age, education, living condition in terms of settlement type, sources of drinking water, criminal activity, and number of ailments significantly affect the degree of Coronavirus related anxiety during COVID-19 pandemic. In this study we have applied two different tools- Composite COVID Stress Index (CCSI) and COVID Anxiety Scale (CAS). For the former one, we applied EFA and obtained four factors affecting respondents’ stress level while for the latter one, we applied a linear regression model to figure out socioenvironmental factors affecting the degree of COVID-19 triggered anxiety. Though each of the said tools (i.e., CCSI and CAS) were used separately, concerned chi squared value implies that there exists a statistically significant difference between the observed and expected values of CCSI and CAS (χ2(431) = 523.12 (p < 0.001)). Considering the empirical findings, we come up with the following three policy recommendations: First, sufficient medical facilities in terms of testing facility for COVID-19 and its related healthcare services need to be set up not only in coastal urban areas but also other areas of Bangladesh. This would enhance the testing frequency and as a result it would be possible to unveil the actual COVID-19 scenario in Bangladesh. In addition, all COVID-19 related treatments need to be offered at a reasonable cost so that people from all walks of life can afford it if they need it. 1. Environmental modifications, degradation and human health risks



57

Appendix 1

Second, presence of at least one psychological consultant in each medical center or hospital is very important during and after COVID-19 pandemic across Bangladesh. COVID-19 triggered degree of fear, anxiety, stress, depression, loneliness, and other mental issues need to be addressed with proper importance. In addition, psychological health services need to be ensured for the COVID-19 infected patients, suspected COVID-19 patients, quarantined individuals, and healthcare providers. This is also suggested by the study of Mamun and Griffiths (2020). Third, people are availing various online services, especially consultation with doctors and specialists for their both physical and mental health. However, in some cases people may adopt inappropriate medication and practice by consulting and/or following misleading information from noncredible online sources due to their knowledge limitation. These may result significant adverse consequences for them in many cases. Thus, it is important for the concerned agencies in Bangladesh to ensure authentic and credible information sources and advisories for the people to fight against the COVID-19 related issues and situation. As a final remark, we would like to suggest conducting comprehensive future studies incorporating both sociodemographic and socioenvironmental factors on mental health during COVID-19, impact of socioenvironmental factors on mental health after COVID-19 as well as issues those are not considered in this current study. Furthermore, same study could be directed with multivariate statistical tools and providing justification with the help of qualitative tools also.

Appendix 1 List of variables, their types, units, and adapted sources. Sl. no.

Variable name

Variable type

Unit of measurement

Adapted source(s)

1

Age

Continuous

Years

Ahorsu et al., 2020 and Brennan et al., 2020

2

Sex

Binary

1 = Male 0 = Otherwise

Li and Liu, 2018

3

Marital status

Binary

1 = Married 0 = Unmarried

Pitpitan et al., 2012

4

Religion

Categorical

1 = Muslim 2 = Hindu 3 = Others

Lee, 2020

5

Education

Continuous

Years of schooling

Ahorsu et al., 2020 and Gavin et al., 2020

6

Employment status

Categorical

0 = Unemployed 1 = Police 2 = Healthcare staff 3 = Soldier 4 = Others

Gavin et al., 2020; Pitpitan et al., 2012

7

Income

Continuous

BDT per month

Gavin et al., 2020

8

Expenditure

Continuous

BDT per month

Gavin et al., 2020

9

Family type

Categorical

1 = Nuclear 2 = Joint 3 = Akannoborty

Li and Liu, 2018

10

Household ownership

Categorical

1 = Own 2 = Rental 3 = Sublate

Tunstall et al., 2014

11

Number of rooms

Continuous

Number

Li and Liu, 2018

12

Living environment

Categorical

1 = Most noisy to 5 = Least noisy

Li and Liu, 2018

13

Security status

Categorical

1 = Highly insecured to 5 = Highly secured

Li and Liu, 2018 (continued)

1. Environmental modifications, degradation and human health risks

58

5.  Socio-environmental factors affecting mental health of people during COVID-19 in coastal urban areas of Bangladesh

Sl. no.

Variable name

Variable type

Unit of measurement

Adapted source(s)

14

Rate of criminal activity

Categorical

1 = Very low to 5 = Very high

Li and Liu, 2018

15

Source of drinking water

Categorical

1 = Own pump 2 = Supply line 3 = Boiled or filtered 4 = Bottle water

Pitpitan et al., 2012

16

Number of household older than 15 years

Continuous

Number

Pitpitan et al., 2012

17

Number of female household greater than 15 years

Continuous

Number

Pitpitan et al., 2012

18

Number of children younger than 15 years

Continuous

Number

Pitpitan et al., 2012

19

Number of physically challenged household

Continuous

Number

Subramaniama et al., 2020

20

Number of mentally challenged household

Continuous

Number

Subramaniama et al., 2020

21

Times of household illness

Continuous

Number

Lee, 2020

22

Types of illness

Categorical

0 = No illness 1 = Fever, cold, cough, headache 2 = Only fever 3 = Asthma, rheumatics, distaste 4 = Others

Lee, 2020

23

Days of sufferings each time

Continuous

Number

Lee, 2020

24

Consulted a doctor

Binary

1 = Yes 0 = No

Lee, 2020

25

Days to recover

Continuous

Number

Lee, 2020

26

Medical service

Categorical

1 = Very dissatisfied to 5 = Very satisfied

Taylor et al., 2020

27

Join office

Binary

1 = Yes 0 = No

Taylor et al., 2020

28

Worried of affecting others

Categorical

1 = Highly disagreed to 5 = Highly agreed

Ahorsu et al., 2020 and Taylor et al., 2020

29

Worried of affecting household members

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

30

Insufficient health care system

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

31

Inefficient health care system

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

32

Insufficient basic hygiene

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

33

Dissent of social distancing

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

34

Grocery item will run out

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

35

Grocery will close down

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

36

Cleaning item will run out

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020 (continued)

1. Environmental modifications, degradation and human health risks



59

Appendix 1

Sl. no.

Variable name

Variable type

Unit of measurement

Adapted source(s)

37

Cold, flu remedies will run out

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

38

Drinking water will run out

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

39

People from other areas are spreading the virus

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

40

Worried of affecting from restaurants

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

41

Worried of affecting from elevator

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

42

Worried of public place

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

43

Worried of others’ coughing or sneezing

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

44

Worried of people having cold around

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

45

Worried of taking cash-change by hand

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

46

Worried of using ATM for cash withdraw

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

47

Worried of mail contamination by the virus

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

48

Trouble in concentrating to anything

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

49

Popped up virus images

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

50

Trouble in sleeping

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

51

Unwillingly think about the virus

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

52

Physical reactions if think about the virus

Categorical

1 = Highly disagreed to 5 = Highly agreed

Ahorsu et al., 2020 and Taylor et al., 2020

53

Having nightmares about Covid-19 virus

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

54

Covid-19 situation affected office services

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

55

Updated with Covid-19 information within country

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

56

Updated about outside countries

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

57

Covid-19 is transfering from one to another

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

58

More dangerous than other virus

Categorical

1 = Highly disagreed to 5 = Highly agreed

Ahorsu et al., 2020 and Taylor et al., 2020

59

Unaware of Covid-19 treatment

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

60

Receiving Covid-19 information

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

1. Environmental modifications, degradation and human health risks

(continued)

60

5.  Socio-environmental factors affecting mental health of people during COVID-19 in coastal urban areas of Bangladesh

Sl. no.

Variable name

Variable type

Unit of measurement

Adapted source(s)

61

Following all instructions

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

62

Created suicidal thoughts

Categorical

1 = Highly disagreed to 5 = Highly agreed

Ahorsu et al., 2020; Lee, 2020 and Taylor et al., 2020

63

Wanted mental support from family

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

64

Had sleeping pill

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

65

Worried of using public transport

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

66

Worried of losing job

Categorical

1 = Highly disagreed to 5 = Highly agreed

Taylor et al., 2020

67

Headache or senseless hearing of Covid-19

Categorical

1 = Not at all 2 = Rarely 3 = Sometimes 4 = Most often 5 = Always

Ahorsu et al., 2020 and Lee, 2020

68

Sleep disturbance

Categorical

1 = Not at all 2 = Rarely 3 = Sometimes 4 = Most often 5 = Always

Ahorsu et al., 2020 and Lee, 2020

69

Asleep thinking of Covid-19

Categorical

1 = Not at all 2 = Rarely 3 = Sometimes 4 = Most often 5 = Always

Ahorsu et al., 2020 and Lee, 2020

70

Loss of appetite while thinking of Covid-19

Categorical

1 = Not at all 2 = Rarely 3 = Sometimes 4 = Most often 5 = Always

Lee, 2020

71

Felt vomiting while thinking of Covid-19

Categorical

1 = Not at all 2 = Rarely 3 = Sometimes 4 = Most often 5 = Always

Lee, 2020

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COVID-19 pandemic, socioeconomic crisis and human stress in resource-limited settings: A case from Bangladesh. Heliyon 6 (5). doi:10.1016/j.heliyon.2020.e04063. Subramaniam, M., Abdin, E., Seow, E., Vaingankar, J. A., Shafie, S., Shahwan, S., . . . Chong, S. A., 2020. Prevalence, socio-demographic correlates and associations of adverse childhood experiences with mental illnesses: Results from the Singapore Mental Health Study. Child Abuse & Neglect, 103, 104447. doi:https://doi.org/10.1016/j.chiabu.2020.104447 Tabachnik, B.G., Fidell, L.S., 2013. Using Multivariate Statistics, sixth ed. Pearson Education, Boston. Tanoue, Y., Nomura, S., Yoneoka, D., Kawashima, T., Eguchi, A., Shi, S., …, Miyata, H., 2020. Mental health of family, friends, and co-workers of COVID-19 patients in Japan. Psychiatry Res. 291. doi:10.1016/j.psychres.2020.113067. Taylor, S., Landry, C.A., Paluszek, M.M., Fergus, T.A., McKay, D., Asmundson, G.J.G., 2020. Development and initial validation of the COVID Stress Scales. J. Anxiety Disord., 72, 102232. doi:10.1016/j.janxdis.2020.102232. Torales, J., O’Higgins, M., Castaldelli-Maia, J.M., Ventriglio, A, 2020. The outbreak of COVID-19 coronavirus and its impact on global mental health. Int. J. Social Psychiatry 66 (4), 317–320. doi:10.1177/0020764020915212. Tunstall, H., Mitchell, R., Pearce, J., Shortt, N., 2014. The general and mental health of movers to more- and less-disadvantaged socioeconomic and physical environments within the UK. Social Sci. Med. 118 (C), 97–107. doi:10.1016/j.socscimed.2014.07.038. Watkins, M.W., 2000. Monte Carlo PCA for Parallel Analysis. Ed & Psych Associates, Pennsylvania. WHO, 2020a. Mental Health and Covid-19. Retrieved from https://www.who.int/teams/mental-health-and-substance-use/covid-19. (Accessed 26 August, 2020). WHO, 2020b. Substantial Investment Needed to Avert Mental Health Crisis. Retrieved from https://www.who.int/news-room/detail/1405-2020-substantial-investment-needed-to-avert-mental-health-crisis. (Accessed 26 August, 2020). WHO, 2020c. WHO Coronavirus Disease (COVID-19) Dashboard. Retrieved from https://covid19.who.int/. (Accessed 26 August, 2020). Yeasmin, S., Banik, R., Hossain, S., Hossain, M.N., Mahumud, R., Salma, N., Hossain, M.M., 2020. Impact of COVID-19 pandemic on the mental health of children in Bangladesh: a cross-sectional study. Child. Youth Serv. Rev., 117, 105277. doi:10.1016/j.childyouth.2020.105277.

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6 Mitigating transboundary risks by integrating risk reduction frameworks of health and DRR: A perspective from COVID-19 pandemic Sivapuram V.R.K. Prabhakara, Rajeev Issarb, Arpah bt. Abu Bakarc, Mariko Yokooa a

Institute for Global Environmental Strategies (IGES), Hayama, Japan b UNDP, Bangkok, Thailand c Universiti Utara Malaysia, Kedah, Malaysia

6.1 Introduction COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as a global pandemic of unprecedented scale. It has tested countries’ preparedness to manage disasters and pandemics with regional and global dimensions. Taking the shape of a transboundary risk, COVID-19 has belied the assumptions related to its linear impact on health and healthcare systems. With its impacts felt across countries, communities, socio-economic sectors and different walks of life, it has truly assumed the proportions of a global disaster requiring mobilization of resources and capacities going beyond what most risk management frameworks and systems are designed to manage. The COVID-19 is not the first transboundary disaster that the countries have faced during recent years. Other notable transboundary disasters that preceded COVID-19 include the SARS outbreak of East Asia in 2003, the global food price crisis of 2008, Bangkok floods of 2011, and the Ebola outbreak in West Africa in 2014. All these disasters affected people and countries outside the countries and regions where they occurred and share similarities with the impacts of COVID-19. HIV/AIDS, though is a global epidemic, doesn’t have distinct outbreak episodes unlike other events described here and doesn’t know to have distinct impacts, described in the following section, that can qualify it to be a transboundary risk. The lessons from these transboundary disasters indicated that the risk reduction frameworks and systems at the national, regional, and international level could not manage these disasters, and have failed to stop them from becoming regional and global disasters. The disaster risk reduction systems and frameworks have been continuously revamped at the international and national levels inspired by the initiatives such as the Sendai Framework for Disaster Risk Reduction, and the Paris Agreement on Climate Change. Both came to existence in 2015. Despite these improvements, a large proportion of national-level risk management systems and frameworks have been designed to respond to and mitigate only those risks that emanate and affect within the borders of the countries. A very little emphasis has gone into mitigating the disaster impacts from spilling over beyond the national boundaries or toward managing the cascading effects of disaster or extreme events happening elsewhere across borders. This left the management of transboundary risks such as pandemics to much less known, less maintained, and archaic acts, such as Epidemic Disease Act 1897 of India, which were developed in an outdated context, while many countries do not have any such instrumentality to deal with the same. Since countries have not been facing epidemics and pandemics as frequently as other natural hazards, and due to the limited awareness on the transboundary risks in general (Prabhakar et al., 2018), developing national response measures Environmental Resilience and Transformation in times of COVID-19. DOI: 10.1016/C2020-0-02703-9

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for addressing such risks has not received sufficient attention. As a result, the expertise and capacities have not been well developed to manage contagious diseases, at the scale of the COVID-19 pandemic. Keeping the above background in view, this paper first provides an overview of new and emerging transboundary risks and places the pandemics and epidemics as important emerging transboundary risks that countries are increasingly facing. It further presents the case of India and Japan in terms of how they responded to the pandemic, reflecting various priorities that these countries have taken up. In the end, the paper presents a risk mitigation and management framework that will help build the capacity of national and international systems and frameworks to manage transboundary risks.

6.2  Impacts of transboundary disasters The COVID-19 and other transboundary disasters have wide-reaching consequences affecting most parts of human lives and national economies. In this section, a summary of the impacts of COVID-19 and other transboundary disasters is presented that also paints a picture of the effectiveness of the national risk management framework.

6.2.1  Impacts of health-related transboundary disasters Prior to Covid-19, SARS, Ebola, and Zika epidemics had provided the initial experience of addressing transboundary health risks. Each of these had long-term impacts across countries and regions with high direct and indirect costs— although the scale and extent of impacts were much lesser than that of Covid-19. Each of these posed a profound equity challenge with a disproportionate impact on the poorest countries with weak health response systems as well as on socio-economically marginalized segments of society. During the Ebola outbreak, restrictions on transport, travel, and movement of labor resulted in nearly 40% of the land in Western Africa going uncultivated and sharp spike in prices of essential food items like rice (Thomas et al., 2014; Fuente et al., 2019). During the SARS outbreak in 2003, the incidence of hoarding of essential supplies such as food was witnessed in China while the spread of the epidemic inflicted wider socio-economic impact across the entire South-East Asian region (Hanna and Hung, 2004). The most recent pandemic COVID-19 had major impact on health systems, employment and economies of countries across the world and in Asia. With more than 50% of the global population in lockdown, for the global economic activity was severely hampered. Some analysis suggested that the economic impact could be similar to that of the Global Financial Crisis of 2009 (GFC; IMF, 2020). In the Asia region, it has affected supply chains and aggregate demand with serious economic repercussions from extended lockdowns in most countries with almost no exceptions. The most affected were the daily workers, those engaged in temporary employment, and migrant workers. Prolonged lockdown has affected all the businesses but the impact on the small and medium enterprises (SMEs) has been severe, with 1.3 billion informal workers affected. 6.7% working hours were reported to be lost globally, including 125 million workers in Asia-Pacific (ILO, 2020). Several of informal workers faced the dual challenge of safeguarding their lives and livelihoods. Others projected least 11 million people in East Asia and the Pacific to fall into poverty in an optimistic scenario (The World Bank, 2020). The nationwide lockdown has affected an estimated 100 million migrant workers in India as they suffered from lack of basic safety and livelihoods (UNDP, 2020). Informal workers were the most effected due to their poor reach to social security networks. Agriculture is an important livelihood source for a majority of population in Asia and a discussion on the impacts of COVID-19 on agriculture is warranted here as an outcome of the decisions made by risk management institutions aftermath of COVID-19. The COVID-19 pandemic has exposed at least two important vulnerabilities of the current food systems, among many others: labor-intensive agriculture systems, and the development of specialized food production zones characterized by monocropping. Though mechanization is on the rise, agriculture is still a laborintensive sector in many Asian countries, barring few highly mechanized pockets. During COVID-19, many Asian countries were having winter crops such as wheat in the fields, which were ready for harvesting sometime during Feb-April coinciding with the COVID-19. There is a direct linkage between large-scale monocropping and longdistance food transportation. Contiguous areas producing a single crop has increased the reliance on the transportation of food over long distances to fulfill diverse nutritional needs, among other factors. Though statistics is yet to come by, emerging evidence indicates several negative outcomes in India and other countries: (1) The large-scale lockdown by governments has severely hampered the labor movement and crop operations in several countries in Asia and beyond (FAO, 2019; Pothan et  al., 2020). This impacted the timely harvest, quantity, and quality of harvested produce with implications for food shortage and food prices in the immediate future. (2) The lockdowns have impacted the perishable food that is meant for long-distance transportation (YaffeBellany and Corkery, 2020; Pothan et al., 2020). (3) The lockdown has led to a food deficit in many markets with an 1. Environmental modifications, degradation and human health risks



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impact on nutritional choices available to people in the short term (Yaffe-Bellany and Corkery, 2020; Pothan et al., 2020). (4) High risk of farmers facing economic hardship to invest in the following rainy season crop as the revenue from the preceding winter season crop was severely affected. In Thailand, the farmers’ income was found to have reduced by 25% and savings by 29%, and debts increased by 25% during COVID-19 (UNESCAP, 2020). On the contrary, the exports of sugar, processed fruits and vegetables, freeze-processed meat, and pet food products grew by 11.9% during the first three months of COVID-19 compared to the previous quarter (January to March 2020). A survey conducted by the Center for Sustainable Agriculture (CSA) in 200 districts in India reported yield loss by 60% of farmers due to the lockdown-related issues (Harvard et al., 2020). Ten percent of farmers couldn’t complete the harvest due to the lockdown-related issues such as labor shortages, low market prices, lack of access to markets, etc. Nearly 22% of farmers couldn’t market the product due to the lockdown and stored the produce. More than 83% of farmers in the states of Punjab, Bihar, and Rajasthan were able to harvest their winter crops, highest among all the states reported in the survey. While the cost of crop harvest was higher for the majority of farmers compared to the previous year, farmers who could engage in family labor reported positive income benefits. The COVID-19 also affected the ability of farmers to sow the following crop due to the high cost of inputs, low income from the previous crop, and labor shortages, on average. In another survey conducted in 47 districts in the northern states in India by the Vikas Anvesh Foundation revealed that 50% of households reduced their food consumption during the COVID-19 which was mainly possible since the majority of them received food supplies through the public distribution system (PRADAN, 2020). These studies have also reported the significant contributions made by the direct cash transfers and employment guarantee schemes such as the National Rural Employment Generation Scheme of India to the purchasing power of the affected populations highlighting the need to put in place robust social safety nets to buffer income shocks. Similar impacts of COVID-19 could be observed in the agriculture sector in Japan. The Annual Report on Food, Agriculture, and Rural Areas in Japan reported a slump in demand for agricultural produce due to school closures, the decline in the prices of flowers, ornamental plants as social events were canceled during the COVID-19 (MAFF, 2020). A significant impact on the labor supply for agriculture was also reported, constraining the agriculture production in the already aging agricultural population in the country. A greater deficit in women in the agricultural workforce was also reported due to the heavy demand in the health sector. While the above impacts on food production and distribution can affect a large section of the society, agricultural laborers and those dependent on agriculture production and food supply chains are most vulnerable to the socioeconomic and nutritional impacts. Food availability and price changes could continue to affect the food consumption of urban poor even after the COVID-19 episode until the economic impacts are stabilized and their purchasing power is restored. Further, several compounding factors are expected to further stress the food availability in the short term. National governments have started using the available food buffer stocks to feed vulnerable sections of the society affected by lockdowns and hence very limited buffer stocks are available to stabilize the postpandemic market prices. No clear strategic interventions by the governments on how to address this impending food shortage problem were apparent during the time of writing this paper. The COVID-19 could have long-term impacts, setting a “new normal,” either planned or unplanned. (1) Emphasis may grow for local food production systems. Governments may rush to promote urban agriculture without robust studies on its impact on the local resources in terms of water, energy, and land especially in and around the urban centers. (2) The emphasis on farm mechanization may further grow with increased demand for off-farm energy inputs. (3) Governments may revamp food buffer stocks, public distribution policies, and related infrastructure with an emphasis on the expansion of cold storage facilities and real-time information on food stocks and food prices. (4) Countries may plan to reduce their dependency on imported food, which can have net positive environmental benefits for some countries. (5) On the contrary, such reduced food imports may have negative consequences for countries such as Japan with a high dependency on imported food and depleted farming population.

6.2.2  The impacts of nonhealth transboundary disasters The impacts of COVID-19 have several commonalities with that of the impacts of other nonhealth transboundary disasters. Understanding such commonalities will help us to visualize the appropriate nature of risk reduction solutions to be developed. Several nonhealth-related transboundary disasters during the recent past provide us the needed understanding. These tsunamis and floods in economically vibrant regions of a country, regional droughts with widespread impacts, and include climate change as a threat multiplier. 1. Environmental modifications, degradation and human health risks

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Disaster risks such as tsunamis, cyclones, floods, and droughts are increasingly casting transboundary impacts due to various factors. On one hand, the magnitude and intensity of disaster events, especially climate-related ones, have amplified due to climate change and other underlying risk drivers. On the other hand, there is a greater interconnection between countries due to socio-economic imperatives. Trade and supply chains, the flow of people, shared natural resources, and linked economies connect countries (Benzie et al., 2018). To add to that, biophysical and socio-economic pathways of the transboundary flow of water resources, biodiversity and ecosystem services, human movement, and trade and supply chains further exacerbates the impact. One country’s adaptation efforts can also affect another country’s resilience and contribute to additional climate risks (Rebecca and Roberts, 2018). One of such transboundary impacts could include countries deciding to put curbs on food exports aftermath of a major flood or drought event that could exacerbate the food prices in the importing countries. Just like health epidemics, disaster events such as the 2004 Indian Ocean Tsunami, droughts in the Horn of Africa and the Sahel, the hurricanes in the Caribbean have assumed trans-national nature with many affecting entire regions or subregions. With the widespread nature of impacts, these incidents highlighted the need for a greater interface between national and regional DM systems. Each of these disasters quickly overwhelmed the national systems and capacities requiring massive international effort and regional support to mount an effective response and recovery effort. Among the interesting cases of disasters with significant transboundary impacts outside the country of disaster occurrence also include the eruption of Eyjafjallajökull volcano in Iceland in 2010 and the floods in Bangkok in 2011. The eruption of Eyjafjallajökull has disrupted air travel in the western and northern Europe. However, a minimal effect on farming in Iceland, the eruption disrupted the weather adversely affecting the flower farmers in Kenya (Justus, 2015). The 2011 Bangkok floods were overwhelming for the people directly affected by the event. It was particularly noticeable for the extensive disruption it caused to the regional and global supply chains and the wide-ranging impacts on the private sector. These two disaster events were perhaps among the few examples of an increasingly evident trend of an in-country disaster causing cascading impact across many countries around the region and/or world. The immediate effects were compounded due to the sudden dislocation they brought to the globalized economy, creating ripple effects across sectors. The economic damage due to the Bangkok floods tantamount to 46.5 billion USD (The World Bank, 2012). The private sector took the largest share of the loss, 90% of total losses, where the multinational entities (MNEs) of Japan dominated (Hayakawa, et al., 2014). Since many Japanese firms provide supplies to their factories in other countries in Asia, their impacts had ripple effects in the supply chains the Japanese companies were participating in. This has further aggravated the impacts of floods which were estimated to be in the range of 10–15 billion USD, where a significant proportion was covered by the Thai insurance companies (Meehan, 2012). The source countries of these MNEs were also severely affected. To regain the lost confidence, the Japanese government had to extend reinsurance support to Thailand (Bank of Thailand, 2012), offered Government of Japan bonds as collateral (BBC, 2011; METI, 2012), and extended other forms of credit and insurance facilities (METI, 2012). This signifies the major risks the MNEs face in the developing countries (Kato and Okubo, 2017). Besides, Japanese insurers were the largest affected among all the foreign insurance companies (with an estimated loss of 1.8 billion USD) (The Institute of Actuaries of Japan, 2013). The impact on the industrial production of the world was estimated to be 2.5% (Haraguchi and Lall, 2015) and 16.2% reduction in industrial production of Japan as a combined effect of floods in Thailand and Great East Japan Earthquake (METI, 2012). The 2008 global food price crisis can also be added to the list of transboundary risks faced by countries during recent years. The crisis was caused by a diversion of crop land to biofuel production Factors such as increasing population, changing consumption trends, and weather abnormalities, the culmination of which were reported to have contributed to the global food price crisis. Extreme measures such as restricting food exports and restricting biofuel production were taken to alleviate the impacts though it was not clear how effective they were (Katz, 2008; MacInnis et al., 2008). The transboundary disasters discussed above have confirmed that we are facing a new paradigm in risks i. e. risks are increasingly becoming globalized and compounding than ever before. Contributing factors are climate change, globalization, and regional economic and social integration, socio-economic processes, livelihood constructs, etc. In all the instances of transboundary disasters discussed above, risks known to be local and to remain local assumed regional and/or global dimensions and impacted millions of people across the world. They overwhelmed national governance, financial, and risk management capacities to manage them and ensure sustainable recovery processes in their aftermath. 1. Environmental modifications, degradation and human health risks



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6.3  Existing risk reduction frameworks and their gaps/challenges Over the years, several risk reduction frameworks have been put in place at the global to local level. Some of the important frameworks are The Sendai Framework for Disaster Risk Reduction (DRR) and the Paris Agreement on Climate Change. Increasingly “shared vulnerabilities” underscore the need for adopting a shared multihazard approach as espoused across different strands of the 2030 Agenda. The Sendai Framework for DRR recognizes the growing imperatives of transboundary risks and states that “…. transboundary cooperation remains pivotal in supporting the efforts to…. . reduce disaster risk……. . Developing countries……need special attention and support to augment domestic resources and capabilities through bilateral and multilateral channels…” (UNDRR, 2015). One of the Guiding Principles calls upon each State to take “the primary responsibility to prevent and reduce disaster risk, through international, regional, sub-regional, transboundary and bilateral cooperation” and to address these, it calls to “foster more efficient planning, create common information systems and exchange good practices and programmes for cooperation and capacity development, in particular to address common and transboundary risks.” (United Nations, 2015: Page 7). The Paris Agreement on Climate Change, while does not make a specific reference to transboundary risks, recognizes the “importance of support on and international cooperation on adaptation efforts, and the importance of taking into account the needs of developing country Parties, especially those that are particularly vulnerable to the adverse effects of climate change.” (UNFCCC, 2015: page 9). The Agenda for Humanity adopted at the World Humanitarian Summit (WHS) underscores the need “…to increase support to countries vulnerable to disaster risks or the negative consequences of climate change….” as part of the Core Responsibility to “Invest in Humanity.” At the same time, the “Commitment to Action” adopted at the WHS mandates the need to “ensure regional and global humanitarian assistance for natural disasters complements national and local efforts.” (UNHCR, 2017: Page 2). At the national level, countries in Asia and elsewhere are developing or revising their national and subnational DRR strategies as envisaged under the Sendai Framework for DRR and their national adaptation plans (NAPs) under the Paris Agreement. However, the lack of a clear understanding of the nature and magnitude of transboundary risks limits their ability to address disaster and climate risks comprehensively. International cooperation on adaptation remains limited to the financing of local projects often ignoring the transboundary risks (Rebecca and Roberts, 2018). Hence, there is a need to relook at these frameworks and strengthen their implementation approaches to address the new and emerging risks such as transboundary risks. Most developing countries in Asia have revamped their disaster management (DM) systems over the past decade or so inspired by global frameworks like the Hyogo Framework for Action (2005–2015) and Sendai Framework for DRR (2015–2030). The improvement in DM systems is significantly apparent with institutional mechanisms and policy frameworks, SOPs to manage postdisaster response, dedicated DM funds, focus on disaster risk mitigation among other measures being the key. However, most of these DM systems are primarily oriented toward managing in-country or localized disasters and have not been designed to address transboundary risks. National disaster risk management approaches have either completely ignored or vaguely covered epidemics and pandemics leaving much to the ad hoc interpretation of DM laws—requiring special interventions to help with the COVID-19 pandemic. For example, India’s official definition of disaster doesn’t clearly cover diseases and its national DM plan talks about diseases as something that needs to be addressed in the aftermath of an event such as typhoons and floods.

6.4  A comparison of responses to COVID-19 by India and Japan Different countries have responded differently to the COVID-19 pandemic. The responses were determined by factors such as the stage of detection of the pandemic, the government’s perceived capacity to manage emergencies as reflected in terms of disaster risk management capacity, health sector preparedness capacity, and the quality of governance. The quality of intervention outcomes was in turn determined by how their societies have responded to government measures. A comparison of approaches taken by India and Japan provides a good case study of responses by the Asian countries. Table 6.1 presents a contrasting picture of how India and Japan responded to the pandemic. This is an emerging picture, valid at the time of writing this paper, and these differences may further emerge over time. Nevertheless, the initial differences in their responses warrant a discussion and provides an interesting case of how countries at different developmental stages may respond to such pandemics. While India has focused on saving the lives during the initial stages of the pandemic, Japan seemed to have focused on safeguarding the economy while minimizing 1. Environmental modifications, degradation and human health risks

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TABLE 6.1  Contrasting responses of India and Japan to COVID-19: major vulnerabilities, capacities, and risks. India

Japan

First case reported

Jan 30th in Kerala state

16th Jan

Strategy (mitigation vs. suppression)

Mitigation

Mitigation

Level of stringency of actions [As on May 7, 2020, Hale et al, 2020]

81.94

47.22

National travel restrictions

Sealed the public movement and public transportation services between states and affected districts.

No internal travel restrictions between cities or prefectures imposed.

International travel restrictions

Started from 26th Jan, with 15 days mandatory quarantine Evacuation of Indian citizens stuck in China, Italy, Iran, etc.

Started from 1st January, no mandatory quarantine. Japanese were brought from Wuhan in several chartered flights.

Economic measures

1st package: 26 billion USD to support poor people (insurance for doctors, money transfer, food supply to the poor for 3 months) 2nd package: 2 million USD for emergency and health systems PM CARES Fund

1st package: 4.5 billion USD for SMEs 2nd package: 9.6 billion USD for SMEs 3rd package: 1 trillion USD as an economic stimulus package

Public support measures

Established Group of Ministers (GoM) on COVID-19 on 11th March. Established national and state-level helplines, help desk, WhatsApp center, etc. Supply of cooked meals to the vulnerable people by the government and NGOs Accommodations to hospital doctors and support staff

Established Novel Coronavirus Response Headquarters on 30th Jan. Not relevant for Japan/Status not known Accommodations (or allowance) to healthcare workers

Health: testing, therapy and cure

Targeted testing, limited to symptomatic patients. Comparatively less number of tests (137 per million population) than Japan, free in government hospitals. The first test kit approved on 24th March that takes 2.5 hours, based on reverse transcription-polymerase chain reaction (RT-PCR) and developed by Mylab. Antibody tests: ICMR validated the US-FSA method and issued guidelines on 4th April. Convalescent plasma therapy was first approved on 10th April.

Targeted testing, limited to symptomatic patients. A comparatively high number of tests (544 per million population) conducted, covered by health insurance. Employed PCR test starting from Feb 18, takes 4–6 hours for results. A new test has been developed by Wako Pure Chemical Corp that takes 2 hours. NIID is testing, no approvals are issued yet for antibody tests.

Regional and international initiatives

10 Million USD support to the SAARC COVID-19 Emergency Fund proposed by India. Export of hydroxychloroquine to needy countries at least cost and large quantities. Commitment to support the G20 statement to fight COVID-19.

Japan is part of the ASEAN+3 mechanism for the health preparedness of the ASEAN region. It is not clear what specific support Japan has committed under the mechanism.

Communication

Direct communication by the Prime Minister with the people of the country Mann Ki Baat, addressed the nation twice on TV. Daily updates by the national health ministry, state chief ministers, and city administration. Aarogya Setu smartphone app. Food and shelters on Google Maps.

The Prime Minister of Japan spoke on several occasions addressing the nation. Regular daily updates are provided by the Minister of Health, Labor and Welfare.

Use of disaster management laws

Activation of the National Disaster Management Act by declaring the COVID-19 as “Notified Disaster” to use disaster management funds at national and state levels.

Declaration of emergency, but not under the Disaster Countermeasures Basic Act, to provide governments special powers to regulate society and provide funding.

Use of health-related laws

Activation of The Epidemic Diseases Act to provide government special powers to regulate society.

Declaration of COVID-19 as infectious disease under the Infectious Diseases Control Law to facilitate treatment.

Note: Most of numeral observations are valid until April 2020. Based on Sources: Ministry of Health, Labor and Welfare, 2020; Ministry of Health and Family Welfare, 2020.

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the human impacts of the pandemic. As the pandemic progressed, it was apparent that the complete lockdown policy of the Government of India early in the pandemic did not suit the Indian context as large-scale unemployment and retreat of migrant workers has disrupted the initial success in curtailing the pandemic. In more than one way, the response by governments to COVID-19 reflected their priorities, whether explicitly stated or not. For example, the responses by the Government of India could be stated as decisive prioritizing the lives of people as opposed to livelihoods and economy. On the other hand, the initial responses by the Government of Japan could be stated as cautious, mostly prioritizing economic wellbeing. The differences in initial priorities are understandable. The Government of India recognizes the weakness of its health sector preparedness to manage pandemics. It knew that any delay in the complete lockdown can put enormous pressure on the health system with a snowball effect on people. However, the priorities and responses changed throughout the pandemic. The Government of Japan has increasingly realized the need for stricter social distancing measures prioritizing human wellbeing. Similarly, India has seen the need to bring focus on the economy and livelihoods of people as negative impacts on livelihoods started to outweigh the benefits of the lockdown as the pandemic progressed. It was apparent that in the end, an equilibrium between economic and social priorities emerged, it became clear that both can’t be considered in isolation. In the case of India, the severe economic impact on the poor and migrant workers pushed the country to ease the restrictions at the cost of the spread of infections. In terms of the mitigation strategy, the Indian government gave less emphasis on livelihoods and more emphasis on lives during the initial phases of the COVID-19. It practiced a complete lockdown of the country with no statelevel exceptions from the midnight of 24 March 2020 (total cases 617) for the initial 21 days [Pilot nation-wide lockdown on 22 March 2020 implemented as “Janata curfew” (self-imposed curfew)]. The schools were “required” to close on 3 March 2020. On the contrary, Japan gave more emphasis on livelihoods and the economy. The emergency measures were effective only from 7th April (total cases 4257) for 1 month. No lockdown, in a strict sense, was announced. Schools were only “recommended” to close on 2 March 2020. Both the countries have considered COVID-19 as a special disaster and declared it as such, which is a common feature to note, to obtain special powers and resources that are otherwise not accessible to governments to manage the pandemic. Several differences in approaches between the two countries are listed in Table 6.1. It is apparent that these differences reflected the respective differences in strengths and weaknesses in these countries in terms of institutions and socio-economic factors. In terms of strengths, India has a young population, strong domestic economy, strong national government, direct cash transfer program for the poor, warm weather conditions, early and strict social distancing measures, isolation and contact tracing measures, less dependence on exports, and low crude oil prices (no reduction in retail prices helped the government with revenue that can be spent on social measures). Initial success achieved through “Bhilwara model,” which was identified as the best model to curtail the spread (involves 6 stages of isolation, mapping of hotspots, door-to-door screening, contact tracing using teams and disinfecting, establishing isolation wards, and help-line for rural areas) gave the country a model to emulate. On the contrary, Japan has high levels of general hygiene, strong communication between local government and people, better rural infrastructure, strong disaster management capacity in general, and “limited impact” on food as Japan didn’t import large quantities from countries that have curtailed food exports due to COVID-19. A high rate of mask usage is common in Japan, especially during the pollen season has contributed to effective mitigation of the spread of the virus. The formation of cluster response teams and the cluster approach for isolation and contact tracing appears to have provided a good model for the country to emulate. More importantly, the standard of living, and cultural level of people, including the presence of strong social etiquette, termed as “mindo” was claimed to have contributed to the significantly low number of infections and death rates in the country. These countries also have several vulnerabilities in terms of COVID-19. For example, India has a high population density, poor sanitation and hygiene conditions, large uneducated population and prevalence of superstitious beliefs, insufficient penetration of health facilities in rural India, insufficient health infrastructure and skills to manage pandemics, large migrant population, and a large number of poor people dependent on daily wages. In the case of Japan, important vulnerabilities include the constitutional challenge of the government to issue relevant countermeasures, dependency on exports, a large proportion of the old population including rural population, high population density in major economic centers, significant regional differences in the medical and welfare resources, lower remote working possibilities especially in small and medium sized enterprises. These vulnerabilities and capacities reflected in terms of the nature of impacts during the course of the COVID19. In the case of India, the impacts were mainly social that was underpinned by the economic impacts. Food security implication for millions of poor people, loss of livelihood for millions of people, loss of crops and perishable food, 1. Environmental modifications, degradation and human health risks

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the large flux of movement of poor people known during recent decades, disruption of the social fabric, fears of economic recession highlights some of the important impacts of COVID-19. In the case of Japan, the impacts were marginal and mainly economic in nature, i. e. fears of economic recession, impact on the tourism industry, and trade. Both the countries have strived to play a regional role to the extent their circumstances allowed them. India has taken a lead role to support countries in the region as evident even during the COVID-19 crisis where it sent its medical teams, medicines and other health infrastructure support to countries like Maldives, Nepal, Bangladesh and others in the region. India has pledged a support of 10 Million USD to the SAARC COVID-19 Emergency Fund. It has helped countries with the export of hydroxychloroquine at least cost and large quantities. It has shown commitment to support the G20 statement to fight COVID-19. Similarly, Japan has increasingly played an important role in the Asia region. As a part of the ASEAN+3 mechanism for the health preparedness of the ASEAN region, it has provided necessary technical support to the group.

6.5  Measures for strengthening risk reduction frameworks COVID-19 and other transboundary risks discussed in this paper highlight the need for integrated risk assessment frameworks. Despite the adoption of the Sendai Framework, further efforts can be taken to effectively manage the transboundary risks. The ISO 31000:2018 Risk Management and the ISO 22301:2019 Business Continuity Management Systems can help to further strengthen the existing international framework such as the Sendai Framework by providing guidelines on managing risks and effective response to both predictable and unpredictable risks. In a context of an organization, Business Continuity Management is part of the overall risk management program that kicks off when an incident occurs (Fig. 6.1). The framework calls for identifying a broad range of principles based on which risk assessments and risk mitigation strategies can be based upon. These principles were drawn from the vast risk reduction experiences based on the available evidence for their effectiveness. The major challenge is in translating these principles into a workable framework that institutions can use for operational purposes. The framework should provide the opportunity to be able to continuously review and revise based on the experiences and emerging new scientific information. It is to be recognized that leadership plays a vital role in determining the effectiveness of the framework and its implementation and hence is considered as central to the process. The ensuing process needs to make sure that procedures and quality measures are implemented for operational effectiveness. ISO 31000 provides a structured risk management process starting from understanding the context of an organization i. e. its business environment, to continuous monitoring of the program. In the implementation of a robust risk management program, leadership roles and commitment are vital, as well as upholding principles to ensure value creation and protection. As more organizations expanding their business globally, transboundary risks are a threat to business sustainability. Safeguarding organizations ensures healthy economic growth of a country. While risk management emphasizes on preparedness, i.e., efforts to manage risks before the occurrence of losses, business continuity management centers on responding to and recovering from post loss incidents. Organizational resilience can be achieved by establishing a risk management program and implementing business continuity management. The principles and the framework as outlined in the ISO 31000 provides a strong foundation in developing effective risk management program to address transboundary risks. The idea of an integrated program centers on the

FIG. 6.1  An integrated risk management framework for addressing uncertain risks. Source: Based on ISO, 2018. 1. Environmental modifications, degradation and human health risks



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FIG. 6.2  Four steps to manage transboundary risks.

fact that risk management has to be an integral part of organization activities. Risks have to be assessed and treated in accordance to the context of the business environment. Similarly, translating this idea to managing transboundary risks, developing such an integrated risk assessment paradigm should recognize the shared risks/interlinkages of risk and exerts collaborative efforts in analyzing and evaluating the shared risks. In addition, coordinated solutions is vital in ensuring effective risk response. Fig. 6.2 reflects the fact that risks are interlinked at different levels. The interconnected nature of risks is a great source of complexity and uncertainty in our understanding of the risks. Our inability to understand and model complex risks continue to be a major limitation to fight new and emerging risks. Our limitation in unearthing hidden vulnerabilities before they “surface” deserves urgent attention. Vulnerabilities form the basis for pressures to translate into adverse impacts. However, our vulnerability assessments are still emerging, and current methods does not factor in the interconnected nature of vulnerabilities and mutually reinforcing nature of seemingly disconnected risks. As a result, vulnerability assessments, and hence the risks assessed, are largely incomplete and fragmented. From this point of view, we are under-estimating risks, and as a result, under-preparing for them at global, regional, and national levels.

6.5.1  Identify and recognize the shared risks Though it has been evident, the interlinked nature of risks from the local to global level has not influenced our way of conducting risk assessments and mitigating risks. This could be due to several issues that are mainly related to limited understanding and data on external or transboundary risks. However, countries are aware of transboundary risks that they are exposed as countries experienced a range of such risks during the recent past as discussed in this paper. There is a need to transform the recognition into action such that the risk identification conducted at the local or national level are informed of the regional and global risks. Recognizing the interconnected nature of risks requires a change in the willingness of policymakers to think beyond boundaries and to provide a mandate to institutions to invest in increasing their understanding of such risks.

6.5.2  Analyze the shared risks that considers hidden vulnerabilities First, analyzing shared risks requires information sharing among countries and regions and sectors within countries. Secondly, the analysis of shared risks should move from factoring “obvious” vulnerabilities and expand to include “hidden vulnerabilities.” Integrated risk assessments are required that factor in the risks across sectors and geographical boundaries which is the major gap in the existing risk assessments that are largely sectoral in nature and seldom consider the risks emanating from outside the “boundaries.”

6.5.3  Share the risk information In addition to integrated risk assessment, sharing of risk information and leadership can further enhance the existing risk management frameworks. Sharing risk information across boundaries has not been done transparently and smoothly. Such sharing of risk information is even more limited with corporations and private entities. The national disaster risk reduction mechanisms and national adaptation planning are designed to address risks that emanate 1. Environmental modifications, degradation and human health risks

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from within their boundaries. The risks emanating from outside the national boundaries are largely not recognized and solutions have not been developed because of the difficulty in understanding these risks and assessing their trajectory, manifestation, and impact. The information that forms the basis for understanding such risks either doesn’t exist or is not being shared across the board. For example, the 2008 food price crisis has demanded to develop a food price early warning mechanism at the global level. Despite the efforts by several international development agencies, a reliable price early warning system couldn’t be developed so far largely because either the required information doesn’t exist or countries are hesitant to share risk information. It is even harder to expect corporations to share information on the risks they are subjected to. Hence, it is suggested that the private companies are encouraged to make risks disclosure in their annual report. A must do for financial risk and for nonfinancial risk is voluntary. Information technologies have been employed to a great extent during the COVID-19 to a scale never seen before and it made a significant difference in the way the information has been shared. This experience has shown the importance of information technologies in managing pandemics. There is a need for a consensus agreement to share risk information and to allow access to risks data freely across countries. A global platform such as Asia-Pacific Economic Cooperation (APEC) may provide an avenue for sharing of risk information. With the right leadership and support from member countries, a more coordinated risk management can be achieved.

6.5.4  Develop globally coordinated solutions Just like the way the risks are increasingly interconnected and globalized, the solutions also need to be connected from the global to regional, national and local scale. This calls for an increased need for seamlessly coordinated risk management processes and instruments from global to a local level based on a universal risk information sharing framework. At the national level, the risk management systems need to be much more coordinated. Some progress has already been taking place at the national level. For example, the disaster risk management systems and the climate change adaptation systems are being well-coordinated in some countries while many other countries are still developing their own approaches. However, coordination of risk mitigation in other areas is far from being satisfactory. For example, the coordination between health and DRM systems deserves great attention, as our experience from COVID-19 suggests. Health systems need to be coordinated with national DM systems so that the capacity of health systems is improved in synchronization with the rest of the DM systems. “Extreme event” is the keyword here where both systems converge. Such coordination also means that health emergencies deserves greater attention in the future than what they have been given so far. Looking at the frequency of pandemics during recent years, the national health systems have to be improved on the same scale as that of the DM systems—and of course, interconnected at all levels. This requires laws and institutional systems for epidemic management at par with the national DM systems. It also means that there is a need to mandate conducting emergency drills and simulation games for epidemics and pandemics: There are no known emergency drills and simulation games for managing pandemics being conducted by governments on a regular basis. It is time for national DRM systems to include pandemics in their emergency drills and simulation games. The overall governance in general and the risk governance in particular assumes importance for managing transboundary risks such as COVID-19. Governments at the national and subnational levels do not have the capacity to manage extreme events. Similarly, in-country systems need to have some coordination platform for interface with subregional, regional, or global systems to benefit from their frameworks, information, capacities, and resources (technical and financial). Different stakeholders in the country including local governments would have to realize that a greater role for the national governments is necessary to help local and regional governments to improve their capacity to manage pandemics. This is important in countries where health is considered as a state subject and national governments do not have much leverage in health matters. Many countries are able to successfully manage COVID-19 when the national and local governments are able to work together putting aside political differences. Building the capacity of different stakeholders forms an integral part of the strategy to develop and implement globally coordinated solutions. The COVID-19 pandemic has caught most NGOs unawares, more than the governments. Usually, NGOs play an important role in managing natural disasters. With the right capacity and predetermined roles, they can come handy in managing future pandemics. The ability of the national and local governments to directly engage with the local communities to follow measures such as self-isolation, self-declaration, and selfquarantine needs to be strengthened and the trust of people in the government to manage pandemics needs to be reinforced through enhancing the quality of risk governance. 1. Environmental modifications, degradation and human health risks



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6.6 Conclusions There is an evidentiary increase in extensive risks while intensive risks have not been effectively mitigated. Factors like climate change, unplanned urbanization, socio-economic issues like inequality, marginalization, discrimination, poverty, etc. coupled with increasing exposure and deepening vulnerabilities are magnifying risks across hitherto “safe” regions and sectors. The greater interaction of risks is leading to an expanding multidimensional risk landscape while weakening governance contexts and inadequate capacities are aggravating the destructive potential of disasters. Addressing one risk at the cost of the others is resulting in skewed risk management practices with diminishing returns as it is leading to elevating the unaddressed risks. Commonalities in the socio-economic processes and developmental constructs are contributing to expanding the geographical occurrence of disaster/climate risks and are magnifying their impacts. COVID-19 is just one of the several transboundary risks that countries have faced during recent times. These experiences have proved that transboundary risks can undermine the capacities of countries to manage risks with short- and long-term consequences. In the short-term, the serious socio-economic effects were apparent on the poor in urban and rural areas. In the long-term, these risks have questioned the risk management practices of governments and institutions and called for reforms in risk management. However, not all is lost. The strengthened DM systems have come to help with the COVID-19, either in terms of using DM funds or using provisions under the laws laid out for DM albeit on an ad hoc basis. There is also evidence to suggest that the national response has been much faster due to improvements in DRM laws, SOPs, and communication systems. Yet, it is true that there is no recent pandemic of a similar scale to compare how best the systems responded. Likewise, COVID-19 has stress-tested the capacity of national risk management systems calling for changes in the way we assess and manage risks. One of the key lessons emerging from the transboundary epidemics and disaster events was that they underscored the need for strengthening national, regional, and global preparedness and response capacities—with the active engagement of local authorities and affected communities—and the need to ensure greater inter-linkages across countries, sectors, and stakeholders. This brings us to the need to consider and analyze the underlying processes, risk drivers, and factors that are increasingly causing high-magnitude multicountry disasters or aggressively contributing to making even the seemingly “localized” or in-country disasters assume regional or global dimension. With risks becoming increasingly systemic, interconnected, and cyclical, many countries are faced with pandemic as well as disaster events like in the Pacific, the Caribbean and the Indian Ocean Rim countries. COVID-19 has added to and aggravated the realized risks with several countries having to manage and recover from multiple disasters. This calls for integrating pandemic as well as disaster preparedness and response in global, regional and national frameworks. Management of transboundary risks requires robust information systems that feed into strategic and integrated risk assessments to identify effective preparedness, response, and mitigation actions. Since the transboundary risks can unearth hidden vulnerabilities, there is a need to identify ways and means of factoring such vulnerabilities into risk assessments. Sharing of risk information is an important part of managing transboundary risks and countries have a long road ahead in establishing a seamless risk information-sharing paradigm. Strengthening risk governance frameworks, systems, and mechanisms at all administrative levels will help augment capacities to manage multidimensional risks. This requires building dedicated institutional capacities backed by a robust and agile policy, legislative, and operational frameworks. Empowering local governments and fostering active community participation can help build trust in the governments and enhance transparency in terms of information sharing for effective risk management. Expanding and deepening impacts across a range of socio-economic sectors has underscored the need for stronger socio-economic preparedness to mitigate the effects. This includes devising and operationalizing contextual social protection measures to build a financial and economic cushion to help communities and development sectors absorb such risks and shocks. Public participation makes a difference and the role of civil society organizations is paramount in fighting against pandemics and other transboundary risks. The criteria to restart economies and “normal lives” should be governed by the principle of building back better and natural wellbeing, realize that human wellbeing is a consequence of natural wellbeing. Prioritizing any other strategy could mean we have not learned a lesson from the pandemic. Another crucial dimension relates to harnessing the potential offered by innovation and rapidly evolving technological landscape with tools such as AI, machine learning, satellite imagery, drone technologies, GIS and other platforms being used to monitor, track, analyze and manage risks in a more timely and coordinated manner than hitherto afforded by the technological limitations and geographical spread. 1. Environmental modifications, degradation and human health risks

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The transboundary nature of disasters is underlining the need for fostering a more seamless interface between national, regional, and global risk management systems and practices. Just like the close in-country vertical integration of DM systems across administrative levels, there is a need to put in place proper protocols and mechanisms for information sharing, early warning, response and recovery coordination including wider risk management practices at the regional and international levels connecting all countries. In the light of this experience, India and Japan’s role in South-Asia, South-East Asia and the wider Indian Ocean Rim countries becomes crucial. Given their investments over the years in risk monitoring, early warning, search, rescue etc., these countries have taken a lead role to support countries in the region and beyond as discussed in the paper. This can justifiably be scaled up by these countries through further strengthening existing mechanisms or promoting newer ones to help cross-fertilize technical expertise, capacities, and systems for risk monitoring, comprehensive multihazard risk management, early warning, early action, etc. with agencies and institutions in the countries in the Asia region. Given the recognition for the leading role in international affairs, as evidenced by the recent unopposed election of India to the UN Security Council, it will be in keeping with India’s growing global stature that it assumes the leadership mantle to help countries and communities address an increasingly manifesting threat of transboundary disasters of multiple origins, be it natural hazards or pandemics or food security issues. After all, the ancient wisdom and philosophy that inspired India for millennia call for considering the world as one family (Vasudev Kutumbakam). The COVID-19 experience has also shown the social and economic resilience of Japan in the wake of the pandemic. The presence of a high standard of living, cultural values, high level of disaster preparedness, hygiene standards, and willingness to engage for the benefit of the society helped the country to become a model for other countries to emulate. The country has made a significant impact on development assistance in the areas of infrastructure, disaster risk reduction, and environmental protection. These experiences and contributions by India and Japan are expected to contribute to strengthening the risk management frameworks and systems in Asia and beyond so that capacity of countries is enhanced to manage and mitigate future pandemics and impacts of other transboundary disasters.

Acknowledgments Authors are grateful for the support received from IGES senior administration in drafting this paper. Authors also gratefully acknowledge the support received from IGES SRF grant, and the support by the Environment Research and Technology Development Fund (2-1801) of the Environmental Restoration and Conservation Agency of Japan.

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P A R T

I I

Water resources: Planning, management and governance 7  An overview of Kuwait’s water resources and a proposed plan to prevent the spread of the Novel Corona Virus (COVID-19) pandemic through Kuwait’s water supply facilities and groundwater system 79 8  Survival of SARS-COV-2 in untreated and treated wastewater—a review 89 9  Wastewater discharge and surface water contamination pre- and postCOVID 19—global case studies 95 10  Addressing associated risks of COVID-19 infections across water and wastewater service chain in asia 103 11  Governance of wastewater surveillance systems to minimize the impact of COVID-19 and future epidemics: cases across asia-pacific 115 12  Impact of COVID-19 lockdown on real-time DO–BOD variation of river Ganga 127 13  Covid-19 and opportunity for integrated management of water– energy–food resources for urban consumption 135 14  COVID-19 lockdown impacts on biochemical and microbiological parameters in southern Indian coast 143

C H A P T E R

7 An overview of Kuwait’s water resources and a proposed plan to prevent the spread of the Novel Corona Virus (COVID-19) pandemic through Kuwait’s water supply facilities and groundwater system A. Akber, A. Mukhopadhyay Water Research Center, Kuwait Institute for Scientific Research, Kuwait

7.1 Prelude Since March 2020, Kuwait has been affected by the COVID-19 pandemic that is still raging in the country despite all precautionary measures adopted by the Government of Kuwait. As a forward-looking country that always strives to investigate the underlying scientific reasons of the problems facing the country and to implement the latest technological innovations and discoveries to mitigate those problems, the Kuwait Government, through the different ministries and public institutions, have planned to initiate studies on different ways the virus spreads in a population and preventive actions that can counter it. This chapter presents an overview of Kuwait’s conventional and nonconventional water resources and ties it to the Novel Corona Virus (COVID-19) pandemic and the possibility of the disease to further spread through the water facilities and the groundwater system. At the outset, it must be acknowledged that the nature of the Novel Corona Virus and its spreading behavior in the ecosystem remain somewhat vague. Therefore, developing a comprehensive study necessitates that all possible contamination pathways of the virus be investigated. The chapter discusses the possible migration paths of the virus and proposes a plan to systemically determine its presence and concentrations. The proposed plan primarily focuses on the process of sample collection, sample preservation, and analysis as well as the treatment techniques that could be used to remove the virus from the contaminated mediums.

7.2 Introduction The State of Kuwait is located in an arid region that is characterized by harsh climatic conditions and scant precipitation (Fig. 7.1). No surface water in the form of rivers or lakes exists within the perimeter of the country. Groundwater is the only natural source of water in the country. It is limited in quantity and is mainly brackish to saline in quality. Very limited amounts of fresh groundwater can be found as lenses floating over the brackish to saline water in the northern part of Kuwait. Groundwater with a salinity of =50% (Wang et al., 2020). The reproduction number (number of individuals prone to infection per infected person) range from 1.4 to 6.5, which is somewhat equal to reproduction number of SARS-CoV-1. SARS-CoV-2 transmission, human to human, mainly depended on the intake of virally contaminated aerosols and droplets recent studies have also suggested the probable fecal oral route of contamination, as the viral RNA was detected in the stool of certain percentage of the infected patients (Zhang et  al., 2020; Lodder and de Roda Husman, 2020). The virally RNA gene getting detected in the stool test, initiated the researches to find its trail in wastewater treatment plants. Several countries have started the wastewater surveillance for the detection of viral SARS-CoV-2 RNA in WWTPs around the world in order to track the future probable cases at a community scale (Bivins et al., 2020). This will also help the policyholders to have a better understanding of the upcoming situations in near future. However, the fecal shedding in wastewater could be especially relevant in low sanitation countries where insufficient or nonexistent treatments of wastewater are applied. Wastewater-based epidemiology (WBE) was coined by (Daughton and Jones-Lepp, 2001) and then was used as first for tracking drugs in wastewater (Zuccato et al., 2008). The approach fundamentally relies on the concept of stability of any substance in the wastewater under ambient condition, and how its concentration can be used to detect the infectious rate in a group of person in a hospital or at a community level. Therefore, various countries on the verge of this methodology have used WBE to be the approach for early prediction of COVID 19 infectious individuals in a society (Kumar et al., 2020b). WBE also helps in determination of the changes in genetic sequence noticed over a period of time and study different viral strains when studied over temporal and spatial scale (Hart and Halden, 2020a). WBE is beneficial in the cases where there are individuals with mild or no symptoms and goes unreported in the system, and ends up infecting others, predicting an actual picture of infectious rate at a community scale (Polo et al., 2020). Environmental Resilience and Transformation in times of COVID-19. DOI: 10.1016/C2020-0-02703-9

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9.  Wastewater discharge and surface water contamination pre- and post- COVID 19—global case studies

FIG. 9.1  Presence of viruses in water environment from different sources.

9.2  Presence in aquatic environment The entrance pathways of the SARS-CoV-2 RNA can be through many sources as shown in figure (Fig. 9.1). All these known and probable sources of contamination tends toward the potential COVID transmission in the aquatic environment (Kumar et  al., 2020a). The contamination of surface water systems with the partially treated effluent may be responsible for the future outbreaks. SARS-CoV-2 detection in the fecal samples of infected people made it very clear to be present in the wastewater as well. The instances of detection of viral RNA in the sewage were reported in (a) Netherland (Medema et al., 2020), (b) Australia (Ahmed et al., 2020), (c) Italy (La Rosa et al., 2020), (d) USA (Wu et al., 2020), (e) France (Wurtzer et al., 2020), and (f) India (Kumar et al., 2020b). The viral RNA concentration detected in the wastewater ranged to as high as 19,000 copies/L in one of the location in Wuhan, admitting COVID 19 patients in the nearby area (Zhang et al., 2020). These presence of SARS-CoV-2 RNA in wastewater samples across the wastewater has helped in predicting the infectious rate within the community, including the ones with mild or asymptomatic infected individuals. These results having advantages also arises the concern of future outbreak, if the virus survives longer in the environment even after certain disinfection procedures (Mallapaty, 2020).

9.2.1  Comparison to other viruses (enveloped/nonenveloped) detected in water SARS (Severe Acute Respiratory Syndrome) and MERS (Middle East Respiratory Syndrome) are enveloped viruses requiring more favorable conditions for their survival rate. Enveloped viruses are assumed to be not persistent in the However, the results showed that T90 value of human Coronavirus (hCoV-229E) changes between 200 and 400 days at 4°C in buffer, surface water, groundwater, and tap water while this value was 20–40 days for the enveloped viruses in wastewater at 4°C (Ye, 2018). Brainard et al. (2017) showed even wastewater temperature increased to 20°C, SARS-CoV has T90 value greater than 3 days (Tables 9.1 and 9.2). The genome sequencing is more than 80% similar of SARS-CoV-2 with its precursor SARS-CoV, which were known for severe respiratory and enteric symptoms (Chan et  al., 2020). The SARS-CoV virus can survive up to 14 days at 4°C, 2 days at 20°C in sewage, howbeit it’s RNA survives for even extended periods (Lodder and de Roda Husman, 2020). Another similarity between SARS-CoV-1 and SARS-CoV-2 (both of them has envelope) is its survival ability ranging from 105–6 genome copies/swab, hinting toward fecal–oral route of contaminations contamination with the viruses are common (Woelfel et al., 2020). Previous reported the surrogate coronaviruses survival ability from few days to several weeks in water and sewages (Casanova et al., 2009). These CoVs survival ability can range from hours to years, infectious nature not considered (Mullis et al., 2012). The surface water in the developing countries remains more probable for the contamination with WWTP effluents in the developing countries. This also paves the way for contamination in the zones of surface and groundwater interactions. The enteric viruses with and average size of 60 nm, having an easy pass-through aquifers (Borchardt et  al., 2003). On the contrary, SARS-CoV-2 are with average 100 nm size has somewhat more resistivity issue while infiltration. The entero and 2. Water resources: planning, management and governance



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9.3  Persistence and removal

TABLE 9.1  Different methods for wastewater quantification for detection of viral RNA gene. Target gene Countries

Concentration

ORF1ab

S

N

E

Genome copies/mL

References

Italy

PEG precipitation of centrifuged supernatant

√

√

-

-

-

La Rosa et al., 2020

Netherland

Centricon ultrafiltration of centrifuged supernatant

-

-

√

√

-

Medema et al., 2020

Spain

Al flocculation—beef extract precipitation

-

-

√

-

104–5

Randazzo et al., 2020

Australia

pH → (-4) Electronegative filtration

-

-

√

-

12

Ahmed et al., 2020

China

PEG precipitation of centrifuged supernatant

√

-

√

-

-

Zhang et al., 2020

France

Ultracentrifugation

-

-

-

√

103–6

Wurtzer et al., 2020

Israel

PEG precipitation of centrifuged supernatant

-

-

-

√

-

Or et al., 2020

USA

PEG precipitation of filtered sample

-

-

√

-

103–4

Wu et al., 2020

Turkey

PEG precipitation of centrifuged supernatant

-

-

-

-

102–5

Kocamemi et al., 2020

Adapted from Farkas et al. (2020).

TABLE 9.2  Presence of different genes of SARS-CoV-2 in surface water. Presence of gene Countries

Wastewater

Italy

Surface water

Orflab

N

E

Reference

√

− (treated)

− (treated)

− (treated)

Rimoldi et al., 2020

√

+ (raw)

+ (raw)

+ (raw)

+ (raw)

+ (raw)

− (treated)

√

adeno viruses which have been detected so far in groundwater are polio, echo, coxsackie, noro, rota, reo, calici, and HAV (Borchardt et al., 2003). These viruses have a strong perspective of contaminating the environmental components including water cycle (Kumar et al., 2020a). It should be noted that the studies about enveloped virus survival in groundwater is limited. In last 2 to 3 decades, the migration of virus to aquatic environment has been mainly focused on the nonenveloped viruses, as these viruses do not have susceptibility issues and most of them survives a certain number of inactivation mechanism (Bosch et  al., 2006). On the other hand, envelope viruses are more fragile in nature and susceptible to elements like heat, UV, pH, which damages their outer envelope and make it loose its infectious nature (Polo et  al., 2020). Some of the other studies, on contrary, have started detecting envelope virus like CoVs in the sewage network too, despite going through a long sewer time, and that too under harsh ambient conditions (Kumar et al., 2020c, 2020d).

9.3  Persistence and removal A study revealed the actual disinfection procedure needed for the complete removal of viral RNA (Zhang et  al., 2020). The presence of RNA with an average concentration of 9.6 × 106 copies/L even after the chloride-based disinfection (NaClO). The author also asks for the reconsideration of WHO and CDC prescribed disinfection measures and the amount of byproduct generated causes ecological risks (if used in higher concentration). (Adelodun et al. 2020) suggest the use of decentralization treatment facilities for the purpose of eradicating the SARS-CoV-2 like 2. Water resources: planning, management and governance

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viruses. The usual WWTPs set up for the purpose of treating community sewage should not be used in parallel with the effluents from health care facilities. Many times improper disposal of infirmary wastes can cause risk of infection (Wang et al., 2020). These types of issues are mostly seen in the low income countries where communities depend on stream and open wells for their daily water use. One of the other measure that can be taken is the community wide testing in the developing countries as well, there has been quite a difference noticed in the testing rate of countries, which somewhat hides the real picture of total infection in the country. WBE is one of such method with which a prior knowledge about the future outbreak can be obtained. There has been a wide scale lack of adequate water quality for the human consumption and sanitation which gives rise to many water borne diseases. There was further trend noticed since the start of the decade where the point of use devices was used for the remediation of these viral pathogens at the sources. These included ZVI filters, bio sand filters, cellulose-based filters, and gravity-based ultrafiltrations. Other inactivation mechanism includes the use of Chlorine, alcohol, UV and sunlight-based mitigations.

9.4  Wastewater-based epidemiology The wastewater hosts a great number of pathogenic viruses (Adriaenssens et al., 2018). Wastewater is already known to host enteric viruses which follows fecal–oral route of transmission and causes gastrointestinal illness. The contaminated wastewater once released into the river system can degrade the environmental health by altering the food chain. The monitoring of wastewater has proved to be beneficial in the past few years. This method has been used to detect the presence of any biological sample of living beings including chemicals like antibiotic drugs, ARGs, MGEs and viral titters (Randazzo et al., 2020). The methodology followed for WBE is shown in figure (Fig. 9.2). The current pandemic has highlighted issues related to the current pace of clinical testing, as there has been lack in the access in most of the economic fragile countries. The analysis of clinical samples at current rate cannot provide a bigger picture of the infectious rate within the community. Therefore, wastewater surveillance at a community scale can be a critical tool for the SARS-CoV-2 infectivity prediction (Polo et al., 2020). WBE has already been proven as the useful surveillance too for the diseases like poliovirus, norovirus and hepatitis A virus (Asghar et al., 2014; Hellmer et al., 2014). Therefore, it can used for the prediction of community scale infectious rate in the current COVID 19 pandemic too (Hart and Halden, 2020a). WBE is effective to predict the future outbreak of disease. The viral shedding in the stool of the infected patients goes up to 3–4 weeks after the first symptoms appears. Another important discussion involves the increase in the RNA concentration in third and

FIG. 9.2  Wastewater-based epidemiology for detection of pathogens. 2. Water resources: planning, management and governance



9.5  Case studies

99

fourth week than the first and second week (Zheng et al., 2020). A better WBE approach can help in detecting the presence of infection in an entire community population. These can help us in reducing the economic damage due to the pandemic time lockdown in different nations and lift the same in some section of the city where the infection is not so prevalent (Hart and Halden, 2020a). The current WBE methodology detects the viral RNA from wastewater sample and helps us to detect the infection prevalence, diverse in the genetics of the virus and geographical effected area (Xagoraraki and O’Brien, 2020). Therefore, to have a correct estimation, the methodology must be sensitive, reproducible and reliable across all the laboratories around the world (Lu et al., 2020). Another advantage of WBE remains the detection of asymptomatic and mild-symptomatic infected individual at community scale and therefore, it is easier to estimate the actual degree of virus circulation. There are many critical step which are involved in the procedure of WBE mentioned below (Polo et  al., 2020) (a) Sampling method: Inside this particular category, timing is more important, as in case of large decentralized plants, the sewage may take 24 hours to reach plant from household, and the amount of viral RNA analyzed may vary according to the time of sampling and will reduce down as the envelope viruses are more susceptible to inactivation. The temperature effect may also incorporate it, as the results may show a good amount of concentration when measured during colder months rather than summer months (Hart and Halden, 2020a, 2020b). (b) Concentration and recovery of the viral titers: two of the strategy were suggested as the best in order to have accurate quantification of the virus (Lu et al., 2020) prefiltration  →  salt addition  →  electro(-)ve membrane filtration and/or prefiltration  → PEGbased separation → overnight standing, the significant concentration method is critically important as there are very few viral load in large volume of wastewater. (c) Quantification is another step needed to be taken care of as most of the time the viral concentration gets effected by the presence of fats, proteins and humic substances (Gibson et al., 2012).

9.5  Case studies There are several countries which have confirmed the presence of viral RNA in surface and wastewater. The suspected fecal oral route of transmission of the virus like the Ebola, HAV, and HEV still remains the hypothetical, but is critically important when more than half of the global population lacks access o proper sanitation (Heller et al., 2020; WHO, 2019). Feces of the COVID 19 infected people reaches the sewer system and undergoes dilution and gets affected by the conditions of pH, temperature, and other substances. Being an envelope virus, they are more prone to losing their infectious nature and envelope than the nonenvelop virus. These factors also results in decrease in their concentration (Foladori et al., 2020). Quito (Ecuador) as in many other cities worldwide, wastewater is directly discharged into natural waters (Guerrero-Latorre et al., 2020). The river samples were taken during the cases were on peak of the pandemic. The samples were concentrated for the detection of N1 and N2 target regions, where analyzed samples were in the range of 2.91 to 3.19 × 105 gc/L for N1 and 2.07 × 105 to 2.22 × 106 gc/L for N2. These results do have an environmental and health impact in the low sanitation lower GDP countries. Arslan et al. (2020) raises serious concern of potential risk of viral disease to the low-income countries. Many of these developing countries lack a sound and reliable health facility, which puts burden on few facilities present there and thereby increasing the load of viral titer in the wastewater facility. The study reported countries like Pakistan and Nigeria, having not a single functional WWTP in its soil, thereby again putting the burden locally available treatment ponds. This untreated/partially treated wastewater then ends up contaminating the local water streams, increasing the chances of re-infection (Afzal et al., 2019; Omole et al., 2019). Even the developed European nation like Italy was not excluded from the pandemic reaching its surface water. River samples from three sites showed positive result for the presence of viral RNA when tested by RT-PCR. This may be attributed to ineffectively treated sewage discharges to natural water systems (Rimoldi et al., 2020). Considering WBE strategy, detection of N3 was noticed at least 6 days before the actual COVID case arrived in Amersfoort, Netherland, when sample in one of the WWTPs (Medema et  al., 2020). Other instances of the first few studies are the detection of viral particles in the sewage with an observed value of around 10 copies/mL (Wu et  al., 2020). Wurtzer et  al. (2020) studied the variation of increase in infected individuals with the increase in viral titer reported in the wastewater samples in range of 50–3000 copies/mL. Randazzo et al. (2020) detected the first traces of SARS-CoV-2 RNA in wastewater of Spain with the concentration of 250 copies/mL. There has been a variation in the amount of gene copies found per mL of the sample. Ahmed et al. (2020) detects only 0.019 to 0.12 genome copies/mL in Australia, while Wu et al. (2020) detects 10–240 genome copies/mL. The 2. Water resources: planning, management and governance

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9.  Wastewater discharge and surface water contamination pre- and post- COVID 19—global case studies

former study validated the amount of viral RNA detected in sewage matched with the prevalence of the infected cases in the city. The later study implies that less number of cases were reported than the predicted after wastewater surveillance. This paves the way for the further improvement in the surveillance like studying the relative changes in the concentration of viral RNA at the WWTP inlet (on daily basis) can help us in predicting the increase in COVID circulation in the community (Medema et al., 2020).

9.6  Environmental implications and policies Owing to the highly contagious nature of human pathogenic viruses, the screening at individual level in some of the highest populous country becomes extremely difficult (Farkas et al., 2020). The less known cases generally go unnoticed due to the individuals having mild symptoms. These mild or no symptoms cases causes’ error in the modeling and assessment of the pandemic model at a global scale. Therefore, creating an urgent need for the prior detection of outbreaks in a community scale and thus provide enough time to come up with a proper protocol to be followed for its mitigation. There are number of studies which have so far detected the presence of viral titers in the treated effluent of from the wastewater treatment plants (WWTPs). The concentration of COVID RNA present was up to 104 genome copies/100 mL in the wastewater effluent. The reduction noticed in WWTPs are in the range of 1–2 log removal (Zhang et al., 2020; Randazzo et al., 2020; Wurtzer et al., 2020). The repercussion of having highly contagious virus in the water environment is still unknown. The ability of the newly pandemic virus to infect the marine animals is reported in some of the studies (Kim et al., 2020). The animals which are living readily close to these effluent outlets are more prone to the viral RNA of this pandemic virus. The next thing which should be looked upon is the ability of these virus remaining infectious still remains doubtful. Further researches regarding the infectious nature of the virus in wastewater should be properly investigated. Most of the time, the viral titers has no effect as when the wastewater gets discharged into surface water, its dilution level is very high. The WBE-based approach is again necessary and need for the hour for at least a year now in order to trace the early warning of the probable infected cases a week later. The issue with COVID 19 is the largest share of the pandemic are asymptomatic individuals, who don’t get easily tracked down and thus ends up infecting other individuals as well (Larsen and Wigginton 2020). WBE can also be thought of as a farsighted technique apart from COVID 19 surveillance as the method can help us track the other pathogen which can be possibly occurring in near future. The correlation between the genome copies detected per L of the wastewater and the number of infected people still remains doubtful. This is due to the fact of its dependency on several other factors, as the most of the cases remain untraceable.

9.7 Conclusion There has been numerous studies which conclude about the viability of the WBE study for planning a better framework for the study of future virus outbreaks. There are several methods for concentrating the wastewater samples in order to further process the extract with the help of PCR methods. The process of real time detection of viral RNA in wastewater and surface water should be further researched. The WBE has proven to be beneficial for having an early idea about the future probable cases. If optimized even further can be applied for the new or old viruses too. The WBE approach can also be made portable, and the current WWTPs can be made to accommodate the quantification and analytical technique in order to have a clearer picture on daily basis, therefore stating the probable peak of any endemic. This approach can also be useful for the other pathogens and emerging contaminants. The biomarkers contained in the wastewater at any WWTP in a city are very crucial for the surveillance system, needed for prediction of infectious rate in a community. The surveillance tells us not only about the societal health, but also accounts for the asymptomatic carriers as they mostly go untested. Although, it is difficult to pinpoint the location of infected individuals, it gives a broader and clear picture of penetration of disease in the society. WBE has ability to be implemented in all categories of nation, be it developing or developed. The effectiveness of the technique has already proven useful for illicit drugs, pathogens detection and therefore can be implemented in any setting trying to monitor the health of community in terms of antibiotic resistance, virus circulation, or any other disease prevalence. 2. Water resources: planning, management and governance



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Kumar, M., Thakur, A.K., Mazumder, P., Kuroda, K., Mohapatra, S., Rinklebe, J., Ramanathan, A.L., Cetecioglu, Z., Jain, S., Tyagi, V.K., Gikas, P., 2020d. Frontier review on the propensity and repercussion of SARS-CoV-2 migration to aquatic environment. J. Hazard. Mater. Lett., 1, 100001. La Rosa, G., Iaconelli, M., Mancini, P., Ferraro, G.B., Veneri, C., Bonadonna, L., Lucentini, L., Suffredini, E., 2020. First detection of SARSCoV-2 in untreated wastewaters in Italy. Sci. Total Environ., 736, p. 139652. Larsen, D.A., Wigginton, K.R., 2020. Tracking COVID-19 with wastewater. Nat. Biotechnol., 38(10), pp. 1151-1153. https://www.nature.com/ articles/s41587-020-0690-1. Lodder, W., de Roda Husman, A.M., 2020. SARS-CoV-2 in wastewater: potential health risk, but also data source. Lancet Gastroenterol. Hepatol., 5 (6), pp. 533–534. Lu, D., Huang, Z., Luo, J., Zhang, X., Sha, S., 2020. Primary concentration—the critical step in implementing the wastewater based epidemiology for the COVID-19 pandemic: a mini-review. Sci. Total Environ., 747, p. 141245. Mallapaty, S., 2020. How sewage could reveal true scale of coronavirus outbreak. Nature 580 (7802), 176–177. Medema, G., Heijnen, L., Elsinga, G., Italiaander, R., Brouwer, A., 2020. Presence of SARS-Coronavirus-2 RNA in sewage and correlation with reported COVID-19 prevalence in the early stage of the epidemic in the Netherlands. Environ. Sci. Technol. Lett., 7 (7), pp. 511–516. Mullis, L., Saif, L.J., Zhang, Y., Zhang, X., Azevedo, M.S., 2012. Stability of bovine coronavirus on lettuce surfaces under household refrigeration conditions. Food Microbiol. 30 (1), 180–186. Omole, D.O., Jim-George, T., Akpan, V.E., 2019. Economic analysis of wastewater reuse in Covenant University. J. Phys.: Conf. Ser., 1299 (1), 012125. 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2. Water resources: planning, management and governance

C H A P T E R

10 Addressing associated risks of COVID-19 infections across water and wastewater service chain in Asia Pham Ngoc Baoa, Vu Duc Canhb a

Natural Resources and Ecosystem Services, Institute for Global Environmental Strategies, Kanagawa, Japan b Department of Urban Engineering, the University of Tokyo, Tokyo, Japan

10.1 Introduction The Asian region continues experiencing severe environmental public health and economic impacts due to poor sanitation and lack of proper wastewater treatment facilities. It is estimated that more than 80% of generated wastewater in Asia was directly discharged into receiving water bodies without adequate treatment (Bao and Kuyama, 2013), causing substantial levels of fecal contamination and microbial risks in drinking water sources, as well as negative impacts on inland and coastal ecosystems. Consequently, many rivers in the region are highly polluted, and more than 2 million people die every year as, mostly children in developing countries, a result of water-related diseases (WHO, 2020b). Moreover, the ongoing global pandemic of novel coronavirus (or COVID-19 pandemic) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is having significant public health impacts at global scale, with more than 115 million infected cases have been reported and over 2.5 million deaths have been confirmed throughout over 200 countries according to the latest information from Johns Hopkins University (JHU, 2020). Since the major transmission, routes of SARS-CoV-2 are inhalation of droplets/particles or aerosols through person-to-person contact or during close unprotected contact between carrier and healthy with SARS-CoV-2 contaminated objects (WHO, 2020a). Such droplets or aerosols landing on surfaces can also spread infection. Many countries have imposed “social distancing” and requested people to wear a mask when going out in public as few of the countermeasures to stop the spread of this infectious disease. Unfortunately, it has been reported and confirmed recently that infectious virions can also be present in human feces (Kitajima et al., 2020; Lin et al., 2020; Wang et al., 2020a; Wang et al., 2005d), which will gradually enter sewerage networks or directly discharged into open environment or nearby water bodies. There are also reports which mention that viral RNA can be persistently shed in feces for a maximum of 33 days even after the patient is tested negative for respiratory viral RNA (Wu et  al., 2020b), while some studies demonstrated that RNA concentration even could be detected in SARS-CoV-2 contaminated wastewater at 4–7 days ahead of case detection (WHO, 2020c). Both viable SARS-CoV-2 and viral RNA can shed in bodily excreta, including saliva, sputum and feces, which are subsequently disposed in wastewater (Kitajima et al., 2020). Coronavirus can be remained infectious in sewage for a much longer period, up to 14 days at 4°C (Wang et al., 2005d) in low-temperature regions. It is estimated that 1.8 billion people globally using fecal-contaminated sources as drinking water, thus, potential COVID-19 transmission risk is expected to increase several folds (Bhowmick et al., 2020), if this contaminated wastewater, fecal wastes and drinking water supply sources are not properly managed. Therefore, safely managed wastewater and fecal wastes from infected, recovering and recovered patients is extremely important, but also pose a significant challenge to stop the spread of infections (e.g., sewerage workers, or population affected by sewage flooding events). This chapter

Environmental Resilience and Transformation in times of COVID-19. DOI: 10.1016/C2020-0-02703-9

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10.  Addressing associated risks of COVID-19 infections across water and wastewater service chain in Asia

aims to (i) highlight a strong need to properly address the existing issue of poor wastewater management in many Asian countries in order to minimize the human health risks associated with SARS-COV-2 infection, (ii) discuss possible routes of SARS-CoV-2 infections and contamination across water and wastewater service chain; and (iii) propose preventive countermeasures to stop possible COVID-19 transmission, particularly it highlights the importance role of using regular viral surveillance in wastewater in the affected areas as an early-warning tool for revealing true scale of the coronavirus outbreak, trends of the pandemic, as well as providing early warnings to the community.

10.2  SARS-CoV-2 in feces and wastewater 10.2.1  SARS-CoV-2 in feces SARS-CoV-2 is known to cause not only respiratory but also gastrointestinal infections (including diarrhea). In a meta-analysis of 60 studies conducted from six countries (China, South Korea, Singapore, Vietnam, United States, and United Kingdom) with a total of 4243 patients infected with SARS-CoV-2, the prevalence of gastrointestinal symptoms and diarrhea were 17.6% and 12.5% (Cheung et al., 2020). Other studies revealed that COVID-19 patients with diarrheal symptoms were in a range from 3.8% to 24.2%, respectively (Guan et al., 2020; Lin et al., 2020; Pan et al., 2020). To date, a number of studies have reported the presence of SARS-COV-2 in stool samples and anal/ rectal swabs from COVID-19 patients by using molecular detection methods (Table 10.1). Although the prevalence of SARS-CoV-2 positivity has varied among studies, the presence of SARS-COV-2 in stool specimens was found relatively common. Lin et  al. (2020) investigated fecal samples of 65 hospitalized patients in Zhuhai, China and found 31 samples (47.7%) were positive. In other studies examined the stool samples from COVID-19 patients, the positive detection rates ranged from 21.4% to 89% (Table 10.1; Chen et  al., 2020b; Wu et  al., 2020b; Zhang et  al., 2020b). Regarding urine specimens, a few studies reported positive results (Chen et al., 2020b; Pan et al., 2020; Wang et al., 2020c; Xiao et al., 2020).

TABLE 10.1  Prevalence of SARS-CoV-2 in feces or anal rectal swabs collected from COVID-19 patients. Detection ratesa Countries

No. patient

No.

%

SARS-CoV-2 concentrationb

Detection methodsc

References

China

95

31/65

47.7

na

rRT-PCR

Lin et al. (2020)

China

42

6/28

21.4

na

rRT-PCR

Chen et al. (2020b)

China

74

41/74

55

na

rRT-PCR

Wu et al. (2020b)

4

China

205

44/153

29

30)

rRT-PCR

Wang et al. (2020c)

China

73

39/73

53.4

na

rRT-PCR

Wu et al. (2020a)

China

178

8/15

53.3

Ct value: 19.5–33.6

RT-qPCR

Zhang et al. (2020b)

2

5

China

82

9/17

53

5.5   ×  10 –1.2  ×  10   copies/mL

RT-qPCR

Pan et al. (2020)

China

57

11/28

39

Ct value: 24–39

rRT-PCR

Chen et al. (2020a)

Korea

46

2/46

4

Ct value: 27.4–31.6

rRT-PCR

Park et al. (2020)

Singapore

18

4/8

50

Ct value: 20–